{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pox-ue9td2l_",
        "outputId": "0dd07d86-75c2-4c93-cc08-b850c7b4b255"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: biocypher in /usr/local/lib/python3.11/dist-packages (0.9.6)\n",
            "Requirement already satisfied: pandas in /usr/local/lib/python3.11/dist-packages (2.2.2)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.11/dist-packages (2.0.2)\n",
            "Requirement already satisfied: networkx in /usr/local/lib/python3.11/dist-packages (3.5)\n",
            "Requirement already satisfied: matplotlib in /usr/local/lib/python3.11/dist-packages (3.10.0)\n",
            "Requirement already satisfied: seaborn in /usr/local/lib/python3.11/dist-packages (0.13.2)\n",
            "Requirement already satisfied: PyYAML>=5.0 in /usr/local/lib/python3.11/dist-packages (from biocypher) (6.0.2)\n",
            "Requirement already satisfied: appdirs in /usr/local/lib/python3.11/dist-packages (from biocypher) (1.4.4)\n",
            "Requirement already satisfied: more_itertools in /usr/local/lib/python3.11/dist-packages (from biocypher) (10.7.0)\n",
            "Requirement already satisfied: neo4j-utils==0.0.7 in /usr/local/lib/python3.11/dist-packages (from biocypher) (0.0.7)\n",
            "Requirement already satisfied: pooch<2.0.0,>=1.7.0 in /usr/local/lib/python3.11/dist-packages (from biocypher) (1.8.2)\n",
            "Requirement already satisfied: rdflib<7.0.0,>=6.2.0 in /usr/local/lib/python3.11/dist-packages (from biocypher) (6.3.2)\n",
            "Requirement already satisfied: tqdm<5.0.0,>=4.65.0 in /usr/local/lib/python3.11/dist-packages (from biocypher) (4.67.1)\n",
            "Requirement already satisfied: treelib==1.6.4 in /usr/local/lib/python3.11/dist-packages (from biocypher) (1.6.4)\n",
            "Requirement already satisfied: colorlog in /usr/local/lib/python3.11/dist-packages (from neo4j-utils==0.0.7->biocypher) (6.9.0)\n",
            "Requirement already satisfied: neo4j<5.0,>=4.4 in /usr/local/lib/python3.11/dist-packages (from neo4j-utils==0.0.7->biocypher) (4.4.12)\n",
            "Requirement already satisfied: toml in /usr/local/lib/python3.11/dist-packages (from neo4j-utils==0.0.7->biocypher) (0.10.2)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.11/dist-packages (from treelib==1.6.4->biocypher) (1.17.0)\n",
            "Requirement already satisfied: python-dateutil>=2.8.2 in /usr/local/lib/python3.11/dist-packages (from pandas) (2.9.0.post0)\n",
            "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.11/dist-packages (from pandas) (2025.2)\n",
            "Requirement already satisfied: tzdata>=2022.7 in /usr/local/lib/python3.11/dist-packages (from pandas) (2025.2)\n",
            "Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.11/dist-packages (from matplotlib) (1.3.2)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.11/dist-packages (from matplotlib) (0.12.1)\n",
            "Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.11/dist-packages (from matplotlib) (4.58.4)\n",
            "Requirement already satisfied: kiwisolver>=1.3.1 in /usr/local/lib/python3.11/dist-packages (from matplotlib) (1.4.8)\n",
            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.11/dist-packages (from matplotlib) (24.2)\n",
            "Requirement already satisfied: pillow>=8 in /usr/local/lib/python3.11/dist-packages (from matplotlib) (11.2.1)\n",
            "Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.11/dist-packages (from matplotlib) (3.2.3)\n",
            "Requirement already satisfied: platformdirs>=2.5.0 in /usr/local/lib/python3.11/dist-packages (from pooch<2.0.0,>=1.7.0->biocypher) (4.3.8)\n",
            "Requirement already satisfied: requests>=2.19.0 in /usr/local/lib/python3.11/dist-packages (from pooch<2.0.0,>=1.7.0->biocypher) (2.32.3)\n",
            "Requirement already satisfied: isodate<0.7.0,>=0.6.0 in /usr/local/lib/python3.11/dist-packages (from rdflib<7.0.0,>=6.2.0->biocypher) (0.6.1)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.11/dist-packages (from requests>=2.19.0->pooch<2.0.0,>=1.7.0->biocypher) (3.4.2)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.11/dist-packages (from requests>=2.19.0->pooch<2.0.0,>=1.7.0->biocypher) (3.10)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.11/dist-packages (from requests>=2.19.0->pooch<2.0.0,>=1.7.0->biocypher) (2.4.0)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.11/dist-packages (from requests>=2.19.0->pooch<2.0.0,>=1.7.0->biocypher) (2025.6.15)\n"
          ]
        }
      ],
      "source": [
        "!pip install biocypher pandas numpy networkx matplotlib seaborn\n",
        "\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import networkx as nx\n",
        "import matplotlib.pyplot as plt\n",
        "import json\n",
        "import random\n",
        "from typing import Dict, List, Tuple, Any"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "try:\n",
        "    from biocypher import BioCypher\n",
        "    from biocypher._config import config\n",
        "    BIOCYPHER_AVAILABLE = True\n",
        "except ImportError:\n",
        "    print(\"BioCypher not available, using NetworkX-only implementation\")\n",
        "    BIOCYPHER_AVAILABLE = False"
      ],
      "metadata": {
        "id": "A-KYiJM1jFIm"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "class BiomedicalAIAgent:\n",
        "    \"\"\"Advanced AI Agent for biomedical knowledge graph analysis using BioCypher\"\"\"\n",
        "\n",
        "    def __init__(self):\n",
        "        if BIOCYPHER_AVAILABLE:\n",
        "            try:\n",
        "                self.bc = BioCypher()\n",
        "                self.use_biocypher = True\n",
        "            except Exception as e:\n",
        "                print(f\"BioCypher initialization failed: {e}\")\n",
        "                self.use_biocypher = False\n",
        "        else:\n",
        "            self.use_biocypher = False\n",
        "\n",
        "        self.graph = nx.Graph()\n",
        "        self.entities = {}\n",
        "        self.relationships = []\n",
        "        self.knowledge_base = self._initialize_knowledge_base()\n",
        "\n",
        "    def _initialize_knowledge_base(self) -> Dict[str, List[str]]:\n",
        "        \"\"\"Initialize sample biomedical knowledge base\"\"\"\n",
        "        return {\n",
        "            \"genes\": [\"BRCA1\", \"TP53\", \"EGFR\", \"KRAS\", \"MYC\", \"PIK3CA\", \"PTEN\"],\n",
        "            \"diseases\": [\"breast_cancer\", \"lung_cancer\", \"diabetes\", \"alzheimer\", \"heart_disease\"],\n",
        "            \"drugs\": [\"aspirin\", \"metformin\", \"doxorubicin\", \"paclitaxel\", \"imatinib\"],\n",
        "            \"pathways\": [\"apoptosis\", \"cell_cycle\", \"DNA_repair\", \"metabolism\", \"inflammation\"],\n",
        "            \"proteins\": [\"p53\", \"EGFR\", \"insulin\", \"hemoglobin\", \"collagen\"]\n",
        "        }\n",
        "\n",
        "    def generate_synthetic_data(self, n_entities: int = 50) -> None:\n",
        "        \"\"\"Generate synthetic biomedical data for demonstration\"\"\"\n",
        "        print(\"🧬 Generating synthetic biomedical data...\")\n",
        "\n",
        "        for entity_type, items in self.knowledge_base.items():\n",
        "            for item in items:\n",
        "                entity_id = f\"{entity_type}_{item}\"\n",
        "                self.entities[entity_id] = {\n",
        "                    \"id\": entity_id,\n",
        "                    \"type\": entity_type,\n",
        "                    \"name\": item,\n",
        "                    \"properties\": self._generate_properties(entity_type)\n",
        "                }\n",
        "\n",
        "        entity_ids = list(self.entities.keys())\n",
        "        for _ in range(n_entities):\n",
        "            source = random.choice(entity_ids)\n",
        "            target = random.choice(entity_ids)\n",
        "            if source != target:\n",
        "                rel_type = self._determine_relationship_type(\n",
        "                    self.entities[source][\"type\"],\n",
        "                    self.entities[target][\"type\"]\n",
        "                )\n",
        "                self.relationships.append({\n",
        "                    \"source\": source,\n",
        "                    \"target\": target,\n",
        "                    \"type\": rel_type,\n",
        "                    \"confidence\": random.uniform(0.5, 1.0)\n",
        "                })"
      ],
      "metadata": {
        "id": "ryoFfR8yjOAT"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "    def _generate_properties(self, entity_type: str) -> Dict[str, Any]:\n",
        "        \"\"\"Generate realistic properties for different entity types\"\"\"\n",
        "        base_props = {\"created_at\": \"2024-01-01\", \"source\": \"synthetic\"}\n",
        "\n",
        "        if entity_type == \"genes\":\n",
        "            base_props.update({\n",
        "                \"chromosome\": f\"chr{random.randint(1, 22)}\",\n",
        "                \"expression_level\": random.uniform(0.1, 10.0),\n",
        "                \"mutation_frequency\": random.uniform(0.01, 0.3)\n",
        "            })\n",
        "        elif entity_type == \"diseases\":\n",
        "            base_props.update({\n",
        "                \"prevalence\": random.uniform(0.001, 0.1),\n",
        "                \"severity\": random.choice([\"mild\", \"moderate\", \"severe\"]),\n",
        "                \"age_of_onset\": random.randint(20, 80)\n",
        "            })\n",
        "        elif entity_type == \"drugs\":\n",
        "            base_props.update({\n",
        "                \"dosage\": f\"{random.randint(10, 500)}mg\",\n",
        "                \"efficacy\": random.uniform(0.3, 0.95),\n",
        "                \"side_effects\": random.randint(1, 10)\n",
        "            })\n",
        "\n",
        "        return base_props\n",
        "\n",
        "    def _determine_relationship_type(self, source_type: str, target_type: str) -> str:\n",
        "        \"\"\"Determine biologically meaningful relationship types\"\"\"\n",
        "        relationships_map = {\n",
        "            (\"genes\", \"diseases\"): \"associated_with\",\n",
        "            (\"genes\", \"drugs\"): \"targeted_by\",\n",
        "            (\"genes\", \"pathways\"): \"participates_in\",\n",
        "            (\"drugs\", \"diseases\"): \"treats\",\n",
        "            (\"proteins\", \"pathways\"): \"involved_in\",\n",
        "            (\"diseases\", \"pathways\"): \"disrupts\"\n",
        "        }\n",
        "\n",
        "        return relationships_map.get((source_type, target_type),\n",
        "                                   relationships_map.get((target_type, source_type), \"related_to\"))\n",
        "\n",
        "    def build_knowledge_graph(self) -> None:\n",
        "        \"\"\"Build knowledge graph using BioCypher or NetworkX\"\"\"\n",
        "        print(\"🔗 Building knowledge graph...\")\n",
        "\n",
        "        if self.use_biocypher:\n",
        "            try:\n",
        "                for entity_id, entity_data in self.entities.items():\n",
        "                    self.bc.add_node(\n",
        "                        node_id=entity_id,\n",
        "                        node_label=entity_data[\"type\"],\n",
        "                        node_properties=entity_data[\"properties\"]\n",
        "                    )\n",
        "\n",
        "                for rel in self.relationships:\n",
        "                    self.bc.add_edge(\n",
        "                        source_id=rel[\"source\"],\n",
        "                        target_id=rel[\"target\"],\n",
        "                        edge_label=rel[\"type\"],\n",
        "                        edge_properties={\"confidence\": rel[\"confidence\"]}\n",
        "                    )\n",
        "                print(\"✅ BioCypher graph built successfully\")\n",
        "            except Exception as e:\n",
        "                print(f\"BioCypher build failed, using NetworkX only: {e}\")\n",
        "                self.use_biocypher = False\n",
        "\n",
        "        for entity_id, entity_data in self.entities.items():\n",
        "            self.graph.add_node(entity_id, **entity_data)\n",
        "\n",
        "        for rel in self.relationships:\n",
        "            self.graph.add_edge(rel[\"source\"], rel[\"target\"],\n",
        "                              type=rel[\"type\"], confidence=rel[\"confidence\"])\n",
        "\n",
        "        print(f\"✅ NetworkX graph built with {len(self.graph.nodes())} nodes and {len(self.graph.edges())} edges\")\n",
        "\n",
        "    def intelligent_query(self, query_type: str, entity: str = None) -> Dict[str, Any]:\n",
        "        \"\"\"Intelligent querying system with multiple analysis types\"\"\"\n",
        "        print(f\"🤖 Processing intelligent query: {query_type}\")\n",
        "\n",
        "        if query_type == \"drug_targets\":\n",
        "            return self._find_drug_targets()\n",
        "        elif query_type == \"disease_genes\":\n",
        "            return self._find_disease_associated_genes()\n",
        "        elif query_type == \"pathway_analysis\":\n",
        "            return self._analyze_pathways()\n",
        "        elif query_type == \"centrality_analysis\":\n",
        "            return self._analyze_network_centrality()\n",
        "        elif query_type == \"entity_neighbors\" and entity:\n",
        "            return self._find_entity_neighbors(entity)\n",
        "        else:\n",
        "            return {\"error\": \"Unknown query type\"}\n",
        "\n",
        "    def _find_drug_targets(self) -> Dict[str, List[str]]:\n",
        "        \"\"\"Find potential drug targets\"\"\"\n",
        "        drug_targets = {}\n",
        "        for rel in self.relationships:\n",
        "            if (rel[\"type\"] == \"targeted_by\" and\n",
        "                self.entities[rel[\"source\"]][\"type\"] == \"genes\"):\n",
        "                drug = self.entities[rel[\"target\"]][\"name\"]\n",
        "                target = self.entities[rel[\"source\"]][\"name\"]\n",
        "                if drug not in drug_targets:\n",
        "                    drug_targets[drug] = []\n",
        "                drug_targets[drug].append(target)\n",
        "        return drug_targets\n",
        "\n",
        "    def _find_disease_associated_genes(self) -> Dict[str, List[str]]:\n",
        "        \"\"\"Find genes associated with diseases\"\"\"\n",
        "        disease_genes = {}\n",
        "        for rel in self.relationships:\n",
        "            if (rel[\"type\"] == \"associated_with\" and\n",
        "                self.entities[rel[\"target\"]][\"type\"] == \"diseases\"):\n",
        "                disease = self.entities[rel[\"target\"]][\"name\"]\n",
        "                gene = self.entities[rel[\"source\"]][\"name\"]\n",
        "                if disease not in disease_genes:\n",
        "                    disease_genes[disease] = []\n",
        "                disease_genes[disease].append(gene)\n",
        "        return disease_genes\n",
        "\n",
        "    def _analyze_pathways(self) -> Dict[str, int]:\n",
        "        \"\"\"Analyze pathway connectivity\"\"\"\n",
        "        pathway_connections = {}\n",
        "        for rel in self.relationships:\n",
        "            if rel[\"type\"] in [\"participates_in\", \"involved_in\"]:\n",
        "                if self.entities[rel[\"target\"]][\"type\"] == \"pathways\":\n",
        "                    pathway = self.entities[rel[\"target\"]][\"name\"]\n",
        "                    pathway_connections[pathway] = pathway_connections.get(pathway, 0) + 1\n",
        "        return dict(sorted(pathway_connections.items(), key=lambda x: x[1], reverse=True))\n",
        "\n",
        "    def _analyze_network_centrality(self) -> Dict[str, Dict[str, float]]:\n",
        "        \"\"\"Analyze network centrality measures\"\"\"\n",
        "        if len(self.graph.nodes()) == 0:\n",
        "            return {}\n",
        "\n",
        "        centrality_measures = {\n",
        "            \"degree\": nx.degree_centrality(self.graph),\n",
        "            \"betweenness\": nx.betweenness_centrality(self.graph),\n",
        "            \"closeness\": nx.closeness_centrality(self.graph)\n",
        "        }\n",
        "\n",
        "        top_nodes = {}\n",
        "        for measure, values in centrality_measures.items():\n",
        "            top_nodes[measure] = dict(sorted(values.items(), key=lambda x: x[1], reverse=True)[:5])\n",
        "\n",
        "        return top_nodes\n",
        "\n",
        "    def _find_entity_neighbors(self, entity_name: str) -> Dict[str, List[str]]:\n",
        "        \"\"\"Find neighbors of a specific entity\"\"\"\n",
        "        neighbors = {\"direct\": [], \"indirect\": []}\n",
        "        entity_id = None\n",
        "\n",
        "        for eid, edata in self.entities.items():\n",
        "            if edata[\"name\"].lower() == entity_name.lower():\n",
        "                entity_id = eid\n",
        "                break\n",
        "\n",
        "        if not entity_id or entity_id not in self.graph:\n",
        "            return {\"error\": f\"Entity '{entity_name}' not found\"}\n",
        "\n",
        "        for neighbor in self.graph.neighbors(entity_id):\n",
        "            neighbors[\"direct\"].append(self.entities[neighbor][\"name\"])\n",
        "\n",
        "        for direct_neighbor in self.graph.neighbors(entity_id):\n",
        "            for indirect_neighbor in self.graph.neighbors(direct_neighbor):\n",
        "                if (indirect_neighbor != entity_id and\n",
        "                    indirect_neighbor not in list(self.graph.neighbors(entity_id))):\n",
        "                    neighbor_name = self.entities[indirect_neighbor][\"name\"]\n",
        "                    if neighbor_name not in neighbors[\"indirect\"]:\n",
        "                        neighbors[\"indirect\"].append(neighbor_name)\n",
        "\n",
        "        return neighbors\n",
        "\n",
        "    def visualize_network(self, max_nodes: int = 30) -> None:\n",
        "        \"\"\"Visualize the knowledge graph\"\"\"\n",
        "        print(\"📊 Creating network visualization...\")\n",
        "\n",
        "        nodes_to_show = list(self.graph.nodes())[:max_nodes]\n",
        "        subgraph = self.graph.subgraph(nodes_to_show)\n",
        "\n",
        "        plt.figure(figsize=(12, 8))\n",
        "        pos = nx.spring_layout(subgraph, k=2, iterations=50)\n",
        "\n",
        "        node_colors = []\n",
        "        color_map = {\"genes\": \"red\", \"diseases\": \"blue\", \"drugs\": \"green\",\n",
        "                    \"pathways\": \"orange\", \"proteins\": \"purple\"}\n",
        "\n",
        "        for node in subgraph.nodes():\n",
        "            entity_type = self.entities[node][\"type\"]\n",
        "            node_colors.append(color_map.get(entity_type, \"gray\"))\n",
        "\n",
        "        nx.draw(subgraph, pos, node_color=node_colors, node_size=300,\n",
        "                with_labels=False, alpha=0.7, edge_color=\"gray\", width=0.5)\n",
        "\n",
        "        plt.title(\"Biomedical Knowledge Graph Network\")\n",
        "        plt.axis('off')\n",
        "        plt.tight_layout()\n",
        "        plt.show()"
      ],
      "metadata": {
        "id": "KDJiPXEBjU5M"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "    def run_analysis_pipeline(self) -> None:\n",
        "        \"\"\"Run complete analysis pipeline\"\"\"\n",
        "        print(\"🚀 Starting BioCypher AI Agent Analysis Pipeline\\n\")\n",
        "\n",
        "        self.generate_synthetic_data()\n",
        "        self.build_knowledge_graph()\n",
        "\n",
        "        print(f\"📈 Graph Statistics:\")\n",
        "        print(f\"   Entities: {len(self.entities)}\")\n",
        "        print(f\"   Relationships: {len(self.relationships)}\")\n",
        "        print(f\"   Graph Nodes: {len(self.graph.nodes())}\")\n",
        "        print(f\"   Graph Edges: {len(self.graph.edges())}\\n\")\n",
        "\n",
        "        analyses = [\n",
        "            (\"drug_targets\", \"Drug Target Analysis\"),\n",
        "            (\"disease_genes\", \"Disease-Gene Associations\"),\n",
        "            (\"pathway_analysis\", \"Pathway Connectivity Analysis\"),\n",
        "            (\"centrality_analysis\", \"Network Centrality Analysis\")\n",
        "        ]\n",
        "\n",
        "        for query_type, title in analyses:\n",
        "            print(f\"🔍 {title}:\")\n",
        "            results = self.intelligent_query(query_type)\n",
        "            self._display_results(results)\n",
        "            print()\n",
        "\n",
        "        self.visualize_network()\n",
        "\n",
        "        print(\"✅ Analysis complete! AI Agent successfully analyzed biomedical data.\")\n",
        "\n",
        "    def _display_results(self, results: Dict[str, Any], max_items: int = 5) -> None:\n",
        "        \"\"\"Display analysis results in a formatted way\"\"\"\n",
        "        if isinstance(results, dict) and \"error\" not in results:\n",
        "            for key, value in list(results.items())[:max_items]:\n",
        "                if isinstance(value, list):\n",
        "                    print(f\"   {key}: {', '.join(value[:3])}{'...' if len(value) > 3 else ''}\")\n",
        "                elif isinstance(value, dict):\n",
        "                    print(f\"   {key}: {dict(list(value.items())[:3])}\")\n",
        "                else:\n",
        "                    print(f\"   {key}: {value}\")\n",
        "        else:\n",
        "            print(f\"   {results}\")\n",
        "\n",
        "    def export_to_formats(self) -> None:\n",
        "        \"\"\"Export knowledge graph to various formats\"\"\"\n",
        "        if self.use_biocypher:\n",
        "            try:\n",
        "                print(\"📤 Exporting BioCypher graph...\")\n",
        "                print(\"✅ BioCypher export completed\")\n",
        "            except Exception as e:\n",
        "                print(f\"BioCypher export failed: {e}\")\n",
        "\n",
        "        print(\"📤 Exporting NetworkX graph to formats...\")\n",
        "\n",
        "        graph_data = {\n",
        "            \"nodes\": [{\"id\": n, **self.graph.nodes[n]} for n in self.graph.nodes()],\n",
        "            \"edges\": [{\"source\": u, \"target\": v, **self.graph.edges[u, v]}\n",
        "                     for u, v in self.graph.edges()]\n",
        "        }\n",
        "\n",
        "        try:\n",
        "            with open(\"biomedical_graph.json\", \"w\") as f:\n",
        "                json.dump(graph_data, f, indent=2, default=str)\n",
        "\n",
        "            nx.write_graphml(self.graph, \"biomedical_graph.graphml\")\n",
        "            print(\"✅ Graph exported to JSON and GraphML formats\")\n",
        "        except Exception as e:\n",
        "            print(f\"Export failed: {e}\")\n",
        "\n",
        "    def export_to_formats(self) -> None:\n",
        "        \"\"\"Export knowledge graph to various formats\"\"\"\n",
        "        if self.use_biocypher:\n",
        "            try:\n",
        "                print(\"📤 Exporting BioCypher graph...\")\n",
        "                print(\"✅ BioCypher export completed\")\n",
        "            except Exception as e:\n",
        "                print(f\"BioCypher export failed: {e}\")\n",
        "\n",
        "        print(\"📤 Exporting NetworkX graph to formats...\")\n",
        "\n",
        "        graph_data = {\n",
        "            \"nodes\": [{\"id\": n, **self.graph.nodes[n]} for n in self.graph.nodes()],\n",
        "            \"edges\": [{\"source\": u, \"target\": v, **self.graph.edges[u, v]}\n",
        "                     for u, v in self.graph.edges()]\n",
        "        }\n",
        "\n",
        "        with open(\"biomedical_graph.json\", \"w\") as f:\n",
        "            json.dump(graph_data, f, indent=2, default=str)\n",
        "\n",
        "        nx.write_graphml(self.graph, \"biomedical_graph.graphml\")\n",
        "\n",
        "        print(\"✅ Graph exported to JSON and GraphML formats\")\n",
        "        \"\"\"Display analysis results in a formatted way\"\"\"\n",
        "        if isinstance(results, dict) and \"error\" not in results:\n",
        "            for key, value in list(results.items())[:max_items]:\n",
        "                if isinstance(value, list):\n",
        "                    print(f\"   {key}: {', '.join(value[:3])}{'...' if len(value) > 3 else ''}\")\n",
        "                elif isinstance(value, dict):\n",
        "                    print(f\"   {key}: {dict(list(value.items())[:3])}\")\n",
        "                else:\n",
        "                    print(f\"   {key}: {value}\")\n",
        "        else:\n",
        "            print(f\"   {results}\")"
      ],
      "metadata": {
        "id": "Dj7emwpTjaRl"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "if __name__ == \"__main__\":\n",
        "    agent = BiomedicalAIAgent()\n",
        "    agent.run_analysis_pipeline()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "0l0Rw1ffe2VM",
        "outputId": "3a33fc17-17b1-416b-e2a6-365100754bb0"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:biocypher:Running BioCypher without schema configuration.\n",
            "/tmp/ipython-input-7-4178500806.py:288: UserWarning: This figure includes Axes that are not compatible with tight_layout, so results might be incorrect.\n",
            "  plt.tight_layout()\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "🚀 Starting BioCypher AI Agent Analysis Pipeline\n",
            "\n",
            "🧬 Generating synthetic biomedical data...\n",
            "🔗 Building knowledge graph...\n",
            "BioCypher build failed, using NetworkX only: 'BioCypher' object has no attribute 'add_node'\n",
            "✅ NetworkX graph built with 27 nodes and 47 edges\n",
            "📈 Graph Statistics:\n",
            "   Entities: 27\n",
            "   Relationships: 48\n",
            "   Graph Nodes: 27\n",
            "   Graph Edges: 47\n",
            "\n",
            "🔍 Drug Target Analysis:\n",
            "🤖 Processing intelligent query: drug_targets\n",
            "   paclitaxel: EGFR\n",
            "\n",
            "🔍 Disease-Gene Associations:\n",
            "🤖 Processing intelligent query: disease_genes\n",
            "   lung_cancer: KRAS\n",
            "   alzheimer: MYC, PIK3CA\n",
            "\n",
            "🔍 Pathway Connectivity Analysis:\n",
            "🤖 Processing intelligent query: pathway_analysis\n",
            "   inflammation: 1\n",
            "   DNA_repair: 1\n",
            "\n",
            "🔍 Network Centrality Analysis:\n",
            "🤖 Processing intelligent query: centrality_analysis\n",
            "   degree: {'genes_EGFR': 0.34615384615384615, 'genes_PIK3CA': 0.19230769230769232, 'drugs_aspirin': 0.19230769230769232}\n",
            "   betweenness: {'genes_EGFR': 0.26619180819180815, 'proteins_insulin': 0.16567032967032966, 'proteins_hemoglobin': 0.13162703962703964}\n",
            "   closeness: {'genes_EGFR': 0.5, 'proteins_EGFR': 0.4727272727272727, 'pathways_inflammation': 0.4482758620689655}\n",
            "\n",
            "📊 Creating network visualization...\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x800 with 1 Axes>"
            ],
            "image/png": 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gIMTExCAjIwPA3NTT9fr5z3+Oqakp3Hrrre7Hs5Tk5ORF0zIjIiIWPF+9vb3Izs5e1OB9fsrj/PMUFxeH7Oxsd/Lr9OnTOHr0KI4dO4ahoSFcv34dZ86cgSzLKybEOjo6AACve93rFr0+L7/88k1fHwCL3ke9vb1ISEhAcHDwgu1ZWVkLfu7s7IQQAp///OcXHfuLX/wiAKz4fM4no4xG44Ltb3nLW9xJ4vmpo681/7rfSJZlfOc730F2djYCAgIQHR2NmJgYNDQ0LPn+eO1zIUkSsrKy0NPTs2D7al731di1axfe85734IknnsDw8PCi28fHxzEzM4Mnnnhi0fP54IMPAlj5+Zx39OhRXLp0CRaLBadPn0ZCQgL27duHkpIS9/utpqZmwfuqt7cXiYmJCxKEwOL37bylnv953/jGN/Dcc8/hmWeeQWVl5U3jJSIi8ifsIUZERLQMhUKBqqoqfPe730VHRwcKCgo8Mq5SqVz1dnFDE21ZlhEbG4unnnpqyfvHxMR4JL7VePvb347a2lp86lOfwp49e6DVaiHLMt74xjdCluV1j3v8+HH8+te/xpvf/GbcdtttqK6uXlBFNW+551C8pun4alVUVODPf/4zLBYLLl26hC984Qvuqp3Tp0/j2rVr0Gq12Lt377JjzD/un/3sZ4iPj190+2sXZ/Ck+WN/8pOfxG233bbkPq9Not0oNzcXANDU1ITy8nL39pSUFHeiNiIiAhMTE4vu+9rqMAD4+te/js9//vN473vfi69+9auIjIyEQqHAxz/+8Q29Pzz5uv/bv/0bfvazn+Hf//3fcc899yy4bT7Gv//7v1+yXxuAZROEN6qoqIDD4cDZs2fdiVZgLlF2+vRptLa2Ynx8fMVE680s9fzPu+222/DSSy/hm9/8JiorK7fkirJERES+woQYERHRCpxOJ4DFlTPzYmJiEBwcjLa2tkW3tba2QqFQLKr8Wq/MzEy8+uqrKC8vX/FLcFpaGoC5iqUbp3GOj4/ftJImMzMTwFxi5JZbbllyn+npafz5z3/Gl7/8ZXzhC19wb5+vkNqou+++G//7v/+L+++/H3fddRdefvnlFR/vctLS0tDQ0ABZlhdUibW2trpvn3f06FE8+eST+OUvfwmXy4UjR45AoVCgoqLCnRA7cuTIsgkZ4G/PXWxs7LLP3Y3HXer5eu37KC0tDSdOnIDZbF5QJdbZ2blgv/nXWa1Wr3js5dx111145JFH8NRTTy1IiK3XM888g6qqKvzoRz9asH1mZgbR0dGL9n/tcyGEQGdn56qSTuuVmZmJv//7v8fjjz++aOGLmJgYhIaGwuVy3fT5XGkBiUOHDkGj0eD06dM4ffo0PvWpTwEAjh07hh/+8If485//7P55XlpaGl599VUYDIYFVWJLvW9vprS0FB/+8Idx11134W1vext++9vfejUxS0REtJ1wyiQREdEyHA4HXn75ZWg0mmVXFlQqlXjDG96A559/fsH0rtHRUfzf//0fKioqoNPpPBLP29/+drhcLnz1q19ddJvT6cTMzAwA4JZbboFarcZjjz22oHLm0Ucfvekx9u3bh4yMDDz66KPu8ebNjzWfFHptVc5qxl+t97znPXj00UdRU1ODt7zlLXA4HGse44477sDIyAh+9atfubc5nU489thj0Gq1OH78uHv7fIXOv//7v6O4uNhdlXb06FH8+c9/xsWLF29axXPbbbdBp9Ph61//+pLxjo+PA5jrL7dnzx785Cc/WTB98JVXXkFLS8uiMR0OB374wx+6t8myjO9///sL9ouNjUVlZSUef/zxZacArqS8vBy33nornnjiCTz//PNL7rOWKiylUrlo/6effnrZPmY//elPYTAY3D8/88wzGB4eXvXKkev1uc99Dg6HA9/85jcXbFcqlXjLW96CZ599Fk1NTYvud+PzGRISAgCL/r0AQGBgIA4ePIhf/OIX6OvrW1AhZrFY8F//9V/IzMxc0DPvjjvugMvlwve+970FY33nO9+BJElrfk5uueUW/PKXv8RLL72E97znPRuq0CMiItpJeImIiIjor1588UV3FcbY2Bj+7//+Dx0dHfiXf/mXFZNaX/va1/DKK6+goqIC//iP/wiVSoXHH38cNptt0RftjTh+/Dg+9KEP4Rvf+AauXr2KN7zhDVCr1ejo6MDTTz+N7373u3jrW9+KmJgYfPKTn8Q3vvEN3HXXXbjjjjtw5coVvPjii0tW59xIoVDgBz/4Ae6++27s2bMHDz74IBISEtDa2orm5mb86U9/gk6nw7Fjx/DNb34TDocDSUlJePnll9Hd3e2xxwoADz/8MKampvDlL38Z9913H5566qlF/cBW8sEPfhCPP/44HnjgAVy6dAnp6el45plncObMGTz66KMLqm+ysrIQHx+PtrY2PPTQQ+7tx44dw2c+8xkAuGlCTKfT4Qc/+AHe8573YN++fXjnO9+JmJgY9PX14Y9//CPKy8vdSY5vfOMbuPPOO1FRUYH3vve9mJqawmOPPYaCgoIF1Yj33HMPDh06hE984hPo7OxEbm4ufve732FqagrAwuqk73//+6ioqEBRURE+8IEPYNeuXRgdHcXZs2cxMDCA+vr6FeP/+c9/jje+8Y245557cPvtt+OWW25BREQERkZG8Oqrr+LUqVOrTsbcdddd+MpXvoIHH3wQR44cQWNjI5566qkFFYs3ioyMREVFBR588EGMjo7i0UcfRVZW1oLFBLxhvkrsJz/5yaLbHnnkEZw4cQKHDx/GBz7wAeTn52NqagqXL1/Gq6++6n4NMjMzER4ejv/5n/9BaGgoQkJCcPjwYXdvr6NHj+KRRx5BWFgYioqKAMwlMHNyctDW1oYHHnhgwXHvvvtuVFVV4d/+7d/Q09ODkpISvPzyy3j++efx8Y9/3F2JuBb33HMPnnzySdx3333Q6XR4/PHH1zwGERHRjuODlS2JiIi2lCeffFIAWPAnMDBQ7NmzR/zgBz8Qsiwv2B+A+OIXv7hg2+XLl8Vtt90mtFqtCA4OFlVVVaK2tnbJ41y4cGHB9i9+8YsCgBgfH1+w/f777xchISGL4n3iiSfE/v37RVBQkAgNDRVFRUXi05/+tBgaGnLv43K5xJe//GWRkJAggoKCRGVlpWhqahJpaWni/vvvd+934sQJAUCcOHFiwTFqamrErbfeKkJDQ0VISIgoLi4Wjz32mPv2gYEBce+994rw8HARFhYm3va2t4mhoaFFz838Y+7u7l70OFbzHAghxEMPPSQAiA9/+MNCCCGOHz8uCgoKFu13//33i7S0tAXbRkdHxYMPPiiio6OFRqMRRUVF4sknn1wyhre97W0CgPjVr37l3ma320VwcLDQaDTCYrEs2H+5x3bixAlx2223ibCwMBEYGCgyMzPFAw88IC5evLhgv2effVbk5eWJgIAAkZ+fL37zm98s+RjGx8fFu971LhEaGirCwsLEAw88IM6cOSMAiF/+8pcL9u3q6hL33XefiI+PF2q1WiQlJYm77rpLPPPMM0s+5teyWCzi0UcfFWVlZUKn0wmVSiXi4+PFXXfdJZ566inhdDoXPE4A4umnn140jtVqFZ/4xCfc77/y8nJx9uxZcfz4cXH8+PFFY/ziF78Qn/3sZ0VsbKwICgoSd955p+jt7V0w5lpe96WkpaWJO++8c9H2jo4OoVQql3wso6Oj4iMf+YhISUkRarVaxMfHi9e//vXiiSeeWLDf888/L/Lz84VKpRIAFrzH/vjHPwoA4vbbb19wn/e///0CgPjRj360KCaDwSD+6Z/+SSQmJgq1Wi2ys7PFt771rSU/iz7ykY8sun93d7cAIL71rW8t2P7f//3fAoD45Cc/ufSTRERE5EckIdbZfZaIiIiIfOK5557Dvffei5qaGo/0/PKV6upqVFVV4emnn8Zb3/pWX4dDREREfoQ9xIiIiIi2MIvFsuBnl8uFxx57DDqdDvv27fNRVERERETbG3uIEREREW1hDz30ECwWC8rKymCz2fCb3/wGtbW1+PrXv76u1TeJiIiIiAkxIiIioi3tda97Hf7jP/4Df/jDH2C1WpGVlYXHHnsMH/3oR30dGhEREdG2xR5iRERERERERETkV9hDjIiIiIiIiIiI/AoTYkRERERERERE5FeYECMiIiIiIiIiIr/ChBgREREREREREfkVJsSIiIiIiIiIiMivMCFGRERERERERER+hQkxIiIiIiIiIiLyK0yIERERERERERGRX2FCjIiIiIiIiIiI/AoTYkRERERERERE5FeYECMiIiIiIiIiIr/ChBgREREREREREfkVJsSIiIiIiIiIiMivMCFGRERERERERER+hQkxIiIiIiIiIiLyK0yIERERERERERGRX2FCjIiIiIiIiIiI/AoTYkRERERERERE5FeYECMiIiIiIiIiIr/ChBgREREREREREfkVJsSIiIiIiIiIiMivMCFGRERERERERER+hQkxIiIiIiIiIiLyK0yIERERERERERGRX2FCjIiIiIiIiIiI/AoTYkRERERERERE5FeYECMiIiIiIiIiIr/ChBgREREREREREfkVJsSIiIiIiIiIiMivMCFGRERERERERER+hQkxIiIiIiIiIiLyK0yIERERERERERGRX2FCjIiIiIiIiIiI/AoTYkRERERERERE5FeYECMiIiIiIiIiIr/ChBgREREREREREfkVJsSIiIiIiIiIiMivMCFGRERERERERER+hQkxIiIiIiIiIiLyK0yIERERERERERGRX2FCjIiIiIiIiIiI/AoTYkRERERERERE5FeYECMiIiIiIiIiIr/ChBgREREREREREfkVJsSIiIiIiIiIiMivMCFGRERERERERER+hQkxIiIiIiIiIiLyK0yIERERERERERGRX2FCjIiIiIiIiIiI/AoTYkRERERERERE5FeYECMiIiIiIiIiIr/ChBgREREREREREfkVJsSIiIiIiIiIiMivMCFGRERERERERER+hQkxIiIiIiIiIiLyK0yIERERERERERGRX2FCjIiIiIiIiIiI/AoTYkRERERERERE5FeYECMiIiIiIiIiIr+i8nUARERERDuZEAKGIQNMoybILhmqABXC0sIQGBbo69CIiIiI/BYTYkREREQeJmSBwQuD6HihA4PnBmGZtMBpc0IIAYVSAVWQCrpkHTJel4HsO7IRnhbu65CJiIiI/IokhBC+DoKIiIhopxi+Moyz3z6LsaYxOG1OaEI0UIeooQpUQZIkyC4ZTosTdqMdTqsTAWEByLo9C4cfOozg6GBfh09ERETkF5gQIyIiIvIA2Snj4uMXUf+Tetj0NoQmhEKj1ax4HyELWKYtME+aEbkrEhWfrUDasbRNipiIiIjIfzEhRkRERLRBslPGqa+dQvOvm6HRahASGwJJktZ0/5neGWhCNaj6chWy3pjlxWiJiIiIiKtMEhEREW3Q+e+fR/OvmxEcHQxtnHZNyTAAUKgUiNgVAafFieovV2PwwqCXIiUiIiIigAkxIiIiog0ZvDCIhp81IEAXsKGVIyVJQlhqGGyzNpx55AxsBpsHoyQiIiKiGzEhRkRERLROLocLtd+qhcPk8EhDfEmSEJYShrHmMdT/pN4DERIRERHRUpgQIyIiIlqn/tp+jF8bR2hS6JqnSS5HqVFCE6LBtd9eg91o98iYRERERLQQE2JERERE69T2uzYIp4A6SO3RcYNjgmEcNuL6n697dFwiIiIimsOEGBEREdE6uOwuDF0YQoAuwONjK9VKCFlgtH7U42MTERERERNiREREROsy3T0Nu9EOdbBnq8PmqQJVGG1gQoyIiIjIG5gQIyIiIloH47ARTqsTqiCVV8ZXB6lhHDbCZXd5ZXwiIiIif+adMzgiIiKiHU52yhCygKRY3ExfCAGn0wmn0wmH0wGH3QGL1QJZlhETHYPAoEBIuEkTfmluHNkpQ6lReulREBEREfknJsSIiIiI1kGhVgASYLVY4RIuuJwuOJ3OuRslQKVSQaVSzSW1ZBkatQYCAg6nA+ZJM0J1odCoNcuOL2QBhUrBZBgRERGRFzAhRkRERLQCWZZhMBig1+vdf2w2G6wjVjiEAxaDBUFhQQgMCIRSqYQkzVV+2ew2GPQGBAYGQhemg9lshuySoQvVweVyQa/XwySZoNPpoFQsTno5LU7EFMZAoWKHCyIiIiJPY0KMiIiICIDNZluQ9DIYDBBCQKFQQKvVQqfTITY2FllZWQgICIDslDH6w1HY9XYEBQa5x3E6ndAb9FAoFIiIjIBCocDU5BTCw8MxMzMDAFAqlYiIiIDdbsf09DQCAgKgDdG6k2kA4LQ5EVcUt9lPAxEREZFfYEKMiIiI/IYsyzCZTAsSXxaLBQAQEBAAnU4HnU6HXbt2QavVQqlcfrqiQqVAakUqmn/ZjJC4EAghYDAa4HQ6odPpoFbNrT5psVqgVquXHEuj0SAqKgoWiwUTkxPQarUIDAyEy+qCQqVA4oFE7zwRRERERH6OCTEiIiLacex2+6JpjrIsQ5IkhISEQKfTITIyEunp6QgMDFxQmbUWOXfnoO35NsyOz8KpcEKr1SJMF+a+XQgBo9GIqKioZceQICE4KBiBgYEwGo0wm8yQ9BIi0iOQejR1XXERERER0cqYECMiIqJtSQgBs9mM2dlZ9xRHs9kMYK6hfVhYGHQ6HVJTUxEaGgqVyvOnPYokBZAEWK5ZEJcbB4VyYb8vs9mM4KBgKKSb9wFTSAroQnWwGq2YNE4i8WAinMIJFU/XiIiIiDxOEkIIXwdBREREtByn0+lOeM0nv5xOp7vaKzQ01J38CgoKWne111oYDAbU19cjJCQECZoEvPChF+AwOaBL1rn3kYWMyclJREdFu2OanJxcsVpMyAJTnVNIKU9B2f8rQ0tbC+Li4pCTkwOFgs31iYiIiDyFCTEiIiLyOSEELBbLgimOJpMJwFwD+vneXjqdDqGhoVCr1T6J0263o6mpCRaLBcXFxQgNDQUANP2yCTXfqIEyQAltnBYAoDfooVarFzTcXykhJmSB6e5phCaE4s7/uRNR2VEQQqCvrw+dnZ3IyclBUlLSpiT8iIiIiHY6JsSIiIjWQgigrw9obweuXwf0ekCSAJ0OyMwEdu8GUlJ8HeWW5XK5FvX2stvtkCQJQUFBCxJfISEhWyb5I8syOjs7MTAwgIKCAsTFLVz9UQiBKz+6gvPfOw8hC4QkhmBWP4uoqChI+NtjWC4h5rA4oO/XQ5esw63fuhUJ+xIW3O50OtHa2oqpqSkUFxcjPDzcK4+TiIiIyF8wIUZERLQadjvwl78Av/kNcOUKYDT+7TYh5pJiABAaChw6BNxzD1BZCXihb9VWJ4SAzWZbkPQyGo0QQkChUCA0NHRB4kuj0fg65BUNDQ2htbUVaWlp2LVr17JJOiEE2n/fjrrv1mGscwyhsaHQxeogKZZPiLnsLpjGTHBanUg8mIij/3YU0TnRy8ZiNpvR0NAApVKJoqIiBAYGeu6BEhEREfkRJsSIiIhupq0NeOQR4Pz5ueRXVNRc4kupXLif0wkYDMDk5FwirLwc+PSngV27fBO3l8myDKPRuCDxZbVaAQCBgYELkl5arXbb9cCanZ1FQ0MDdDod8vPzVz1Nc6h1CK9+41XY2+ywzlihUCqgDlFDFaDCrH4WulAdHGYHnBYnIAGhSaEo+rsiFL27CKqA1SVQJyYm0NTUhPj4eOzevXvbPbdEREREvsaEGBER0Up+//u5ZNjkJJCcDAQF3fw+AGAyAYODQHw88PnPA7fc4t04vWi+2mt+qqPBYIDL5XJXe91Y8RUQELBlpjmul81mQ2NjIxwOB4qLixESErKm+585cwbFxcUQeoGuP3Vh+PIwxlvGYTfaYdAboAvXITg6GHHFcUguS0bG6zKgCVl7lZwQAr29vejq6mJ/MSIiIqI1YkKMiIhoOb/7HfCVrwAOx1xfsLUmG2R5rt9YcDDwta8Bt97qnTg9QAixbLWXRqNZVO2lfG113A4gyzLa29sxPDyMwsJCxMTErHmM8fFx9PX1Yf/+/QvHdsqwTFtw5vQZHK06isDwQI8lr5xOJ65du4bp6Wn2FyMiIiJaJSbEiIiIltLUBHzwg4DZPFcZtt7khRBAby8QHg78+Mdzjfd9yOFwLEh6GQwGOJ1OSJIErVa7IPEVGOi5pM1WJoTA4OAg2trakJmZibS0tHU9biEETp48ibKyMgQEBCy5z8mTJ3H8+PGNhrwks9mM+vp6qFQq9hcjIiIiugkmxIiIiF7LagU+8AHgwgUgK2v9ybB5sgx0dgLHjwP//d9eb7QvhIDZbF6Q+DKbzQAAlUq1IOkVGhoKlR82/p83MzOD+vp6REVFITc3d0PPRX9/PwwGA/Lz85fdx5sJsXkTExNobGxEQkIC+4sRERERLcN/z4CJiIiW8+KLwKVL65smuRSFAkhKAmprgVdfBd74xo2PibmpcvN9veb/OJ1OAEBwcLA76ZWcnIzg4GC/qPZaLavVioaGBsiyjIMHDyI4OHhD481Pt6ysrPRMgBsQHR2NyspK9Pb24sSJE8jNzUViYiJffyIiIqIbMCFGRER0IyGAZ5+dS4R5cspZSAjgcgHPPQfcdtuqE21CCFit1gVJL6PRCCEEVCqVu6F9YmIicnNzV70Sor9yuVxoa2vD2NgYioqKEBUV5ZFxOzs7kZGRsWV6q0mShPT0dCQnJ+PatWvo6upCSUkJwsLCfB0aERER0ZbAhBgREdGNWlrm/qyjofpNRUfPVZ719AAZGQtucrlci5ra2+12AEBgYKC72is+Ph4hISGcBrdGQgj09/ejo6MD2dnZyMvL81jFlMPhwMDAAKqqqjwynifN9xOb7y+mVqtRVFS0bI8zIiIiIn/BhBgREdGN2tsBiwVITPT40CI0FGJsDPrz5zHhcsFgMMBgMECWZSiVSne1V2xsLLKyspi08JCpqSk0NDQgNjYWlZWVHq/iamlp8WiCzRuCg4NRVlaGiYkJ1NbWsr8YERER+T0mxIiIiG7U2Tn3/w0kN4QQcLpccDqdcDoccDqdcMkyJABBNhssTU0ILC9HbGwstFotkxJeYrFYUF9fD4VCgcOHDyMoKMjjxzCbzZidnUVxcbHHx/aG+f5iPT097C9GREREfo0JMSIiohtNTq5pdyEEzBbLXPLL6ZzrQYa5qWoqlQoajQbBf53iKAHA1NRcA/fkZM/HTgDmFhtobW3F5OQkiouLERER4bVjNTY2oqioaFsllCRJQkZGBlJSUtDS0sL+YkREROSXmBAjIiK60V8TWqsmSVBIEoKDgqBSqW6eGBECkOX1x0fLEkKgt7cXXV1dyMnJQUFBgVcTVTMzMxBCrDnhJoTYEgk0lUqF4uJimEwmNDQ0sL8YERER+RUmxIiIiG4UHr6mpJgErG0qnkIBhIauOSxa2cTEBBobG5GQkICqqqpNmYba2NiIffv2rek+CoViyyTE5oWEhKCsrAzj4+Oora1FYmIisrOzOZWXiIiIdjQmxIiIiG6UmTnXP0yIDfURW9J8ZVhmpmfH9WM3VjcdOXJk06qbRkZGEBoaipCQkDXdT5IkiLVWIW6SmJiYBf3F8vLykJCQsKWSd0RERESewoQYERHRjXbvBgIC5laaDA727NhG49yYWVmeHdcPOZ1ONDc3Q6/Xo7i4eFP7Xwkh0NLSgoqKijXfdysnxIDF/cU6OzvZX4yIiIh2JCbEiIiIblRYOFfB1d4OpKd7duyJCWDfvrmkG62LEALd3d3o7u5GXl4eSkpKNj2G3t5eJCYmQqPRrPm+CoUC8jboIcf+YkRERLTTMSFGRF43bBhG01gTOqc6MWwchs1pQ7A6GKlhqciKzEJJfAl0ATpfh0k0R6UC3vxm4GtfA+x2YB1JjyXZbHPTMN/8Zs9PxfQTY2NjaGpqQnJy8qb1CXstl8uFrq4uVFZWruv+W71C7LXYX4yIiIh2Kklsp7MyIto2hBC4OHQRv7n2G5zoOYFZ2ywgAAEBhaSALGRIkKBQKBATHIO7d9+Ne/Puxa6IXb4OnQgwGID77gPa2oBduzaewBIC6Oycqw770Y+AtTThJxgMBjQ0NCAoKAiFhYXrqszylGvXriE4OBhpaWnruv+5c+dQUlKCwMBAD0fmfa+tzktMTPR1SERERETrxoQYEXncjHUG3zv/Pfzm2m9gdpgRERSBiMAIKBXKRfvaXXZMmidhsBkQHRyND+7/IN5V9C6olWofRE50g7NngYcemkuGxcdvbKzBQUCtBp54AvDBFL/tym63o7m5GSaTCcXFxdDpfFtJarPZUFtbi8rKynU3mj9//jyKiorWtjLpFjPfv212dpb9xYiIiGjbYkKMiDzq+vR1fOLlT6BlrAXRIdGIDIpc1f1kIWPEOAKLw4LXZbwOX3/91zmNknzvf/8XePRRIDAQiI1de6WYEMDw8Nzqkp/5DPDOd3olzJ1GlmV0dXWhv78f+fn5iN9oQtJDrly5gsTERMTFxa17jAsXLiA/P3/Nq1NuRSaTCfX19QgICEBhYSH7ixEREdG2wh5iROQxvTO9ePjFh9E51YmMiIw1VXkpJAUSQxNhspvwStcrcMkufPsN30aIZvt/aaRt7MEH55Ja//3fwPXrQGrqXKXXatjtQF8fEBoKfOITwDve4d1Yd4iRkRG0tLQgNTUVVVVV667E8jSTyQSj0bihZBgw11R/p1yLDAkJwZEjR9hfjIiIiLYlVogRkUfYXXZ86PcfQu1ALXZF7IJKsf58u9lhxqB+EO/d+158uvzTHoySaJ3OnAG+9S2gtXWuyX5s7FzV2GuTNUIAFgswNga4XEBBwVxl2IEDvol7G9Hr9aivr0doaCgKCgqgXm3icZOcPXsW+fn5G54eePnyZWRnZyM0NNRDkW0N7C9GRERE2w0rxIjII37e8HPUDdYhRZeyoWQYAASrgxERFIFfNf8Kx9KOoTS51ENREq1TeTlQWAg89xzw29/OVYs5HHO3zVfDuFxzCTKNBsjJAe69F7jnHmAHTI3zJpvNhqamJlitVuzduxdardbXIS0yNTUFpVLpkV5ZkiRBlmUPRLW1SJKEXbt2ITU1Fc3Nzejq6kJJSYnP+74RERERLYcVYkS0YVOWKdz7y3thdBiRGOqZqgAhBDqnOlGaXIon/78nt8y0KSI4HMDVq0B7+9zKkdPTc9ujooDs7Lk/JSWAitecViLLMjo6OjA0NISCggLExsb6OqQlCSFw6tQpHDp0yCON8Ovr65GWlobw8PCNB7eFsb8YERERbXU8WyeiDXu562WMmkaREZHhsTElSUKcNg4Now1oGG1ASTxX5qMtQq0GDh6c+0NrJoTA0NAQWltbkZGRsaEVGzfD8PAwIiIiPLYqpCRJO6aH2Erm+4uNjY3hzJkzSE5ORlZWFvuLERER0ZbBsxIi2rAXO1+ESqHa8FTJ1wrVhMLkMKG6p9qj4xKRb8zMzOD06dOYnJzE8ePHsWvXri2dDBNCoLW1FXl5eR4bc6dOmVxObGwsqqqqoFKpUF1djeHhYV+HRERERASAFWJEtEEmuwkdkx0IDfB8g2hJkqBRatAw1uDxsYlo81itVjQ2NsLpdOLAgQMIDg72dUircv36daSkpHi0wf9OWmVyteb7i6WkpKClpQWdnZ3sL0ZEREQ+x4QYEW3I9enrMDvMiA6O9sr4wepgtE+2w+6yQ6PUeOUYROQdLpcL7e3tGB0dRWFhIaKjvfM54Q1OpxM9PT2oqqry6Lj+MmVyKWq1GiUlJTAajaivr0dgYCCKioqg0fCznYiIiDYfp0wS0YYY7AY4ZAfUytVXUDjmV+dbBY1SA4fLAZPdtJ7wiMgHhBDo7+9HdXU1goODcfz48W2VDAOAtrY25OTkeLznlT8nxOZptVqUl5cjJSUFNTU1aG9v96tppERERLQ1MCFGRJtuamoK4+PjwCq+E85/cdzKfYaI6G+mp6dx6tQp6PV6VFZWIi0tbdv9+7VarRgfH0dSUpLHx2ZC7G/m+4splUr2FyMiIqJNxymTRLQhugAd1Ao17C77qpvqx8XGYXRsFKNjo4iNiYWkWP7LskN2IEAZgBB1iKdCJiIvsFgsaGiY6/d36NAhj63K6AtNTU0oLCz0SiLP35rq34wkScjMzERqaiqam5vZX4yIiIg2DRNiRLQhuyJ2IUQTArPDjGD1KhtlS3OVAePj4xgbG0N0TDSUSuWSu5rsJhQnF69pSiYRbR6Xy4XW1lZMTEygqKgIkZGRvg5pQ/R6PWw2m9emePpjU/3VUKvV2LNnj7u/WFBQEAoLC9lfjIiIiLyGUyaJaEOC1cHIicqB3qZf0/0kSUJMTAwUSgUmxidgt9sX7SOEgEN2oDiu2FPhEpGHCCHQ29uL6upq6HQ6HDt2bNsnwwCgsbERxcXe+8zhlMmVzfcXS05ORk1NDTo6OlhRR0RERF7BhBgRbdjtWbdDFjKcsnNN95MkCdFRc9VhszOzsFgsC2432A3QqrWoyvDsKm9EtDGTk5M4efIkzGYzKisrkZKSsu36hC1lfHwcgYGBCA0N9doxOGVydeb7iykUClRXV2NkZMTXIREREdEOw4QYEW3YGzLfgPiQeIwY1/6FRVJIiIyKhCRJMJvNMBgMgJirPhk1jmJfwj4UxBR4IWoiWiuz2Yy6ujpcv34dpaWlyMvLW3a683YjhEBzczMKCrz7ecMpk6s331/s6NGjGBkZQU1NDfT6tVUjExERES2HPcSIaMPCAsPwvn3vw9dPf31tvcT+SqFQIDIyElNTUxCywPTMNBxqB0IDQvHhAx/eEZUnRNuZ0+lES0sLZmZmUFxcjPDwcF+H5HEDAwOIiYlBYGCgV4/DKZNrx/5iRERE5A2sECMij3hHwTtQkVqBAf0AHC7Hmu+vUCoQEREBu90Ol8KF4elhvCP3HdifuN8L0RLRaggh0N3djZMnTyIqKgpHjx7dkckwWZbR3t6OnJwcrx+LUybXb76/WFJSEvuLERER0YYxIUZEHqFWqvGlyi8hPzof3dPdsLsWN8m/GaVKCVWICgP6Abwh8w0osBZgdnbWC9ES0c2Mj4+juroadrsdVVVVSEpK2rHVml1dXcjIyIBK5f3CeU6Z3Li4uDj2FyMiIqINkwTPyojIg/pn+/HJVz6J+pF6RARFICooalVfomUhY1A/CIfLgarUKtweeDuOHj6KS5cuIScnB4mJiZsQPREZjUY0NDQgICAAhYWFCAgI8HVIXuVwOHD69GlUVVVtSsJvYGAANpsNmZmZXj+WP3A4HGhubobRaERJSYlXF0QgIiKinYUJMSLyOIPNgCcuPYFfNv0SerseYQFhiAyKhFqpXrCfEAI2lw0T5gmYHWYkaBPwjwf/EW/OezNmp2fR2NiIsrIyXL58GVFRUdi9e7ePHhHRzjefWDAYDCgpKYFOp/N1SJuioaEB0dHRm5Z0HxwchMViQVZW1qYcz18YDAY0NDSwvxgRERGtGhNiROQ1jaONeL7tebzY+SJmrDOQhbxoqpBGqUG8Nh735t6Lu3PuRrIu2X3b6Ogo2tracOTIEbS2tsJqtWLfvn1QKDjbm8hThBC4fv06ent7kZeXh4SEBF+HtGksFgvOnz+PY8eObdp00KGhIZhMJmRnZ2/K8fzN6OgompubkZKSgszMTP6+ICIiomUxIUZEXjdlmULLeAs6pzoxbBiGS7igUWqQGpaKrMgs5MfkL7sy5eDgIHp6elBWVob+/n709vaitLSUV/+JPMDfkwfnz59HVlYWIiMjN+2YIyMjmJ2d3ZQG/v5KCIGuri709fUhPz8f8fHxvg6JiIiItiAmxIhoy+vt7cXIyAgOHTqEyclJ1NfX4/Dhw9Bqtb4OjWhbMhgMqK+vR0hICAoKCvwywTw7O4uWlhaUlZVt6nFHR0cxPT2N3NzcTT2uP7Lb7WhubobJZGJ/MSIiIlqECTEi2ha6urowMzODffv2wWw249y5cygqKkJMTIyvQyPaNux2O5qammCxWFBcXOzXCYLTp09j3759CAkJ2dTjjo2NYXJyEnl5eZt6XH82318sODjYbxPAREREtBgTYkS0bbS2tsJms6GkpAQOhwN1dXVITk5GRkaGr0Mj2tJkWUZXVxf6+/tRUFCAuLg4X4fkU6OjoxgaGsLevXs3/dgTExMYHR1FQUHBph/b381PEU5NTUVmZuam9Y0jIiKircm/moUQ0baWm5sLhUKBlpYWqNVqVFRUYGZmBvX19Yua9RPRnKGhIVRXV0OhUKCqqsrvk2FCCLS0tPgsISVJEj+vfCQuLg5VVVUAgBMnTmB0dNTHEREREZEvsUKMiLYVIQSuXLmC0NBQ9yptnZ2dGB0dxeHDh6FSqXwcIdHWMDs7i4aGBuh0OuTn50OtVvs6pC2ht7cXFovFZz28pqamMDg4iKKiIp8cn+awvxgRERExIUZE244QAhcuXEBMTIx7uuT8VJiysjIEBQX5OEIi37HZbGhsbITD4UBxcfGm98jaylwuF6qrq1FZWQmlUumTGGZmZtDb24uSkhKfHJ8W4gITRERE/osJMSLaloQQqKurQ0pKCpKTkwEAer0eFy5cwL59+xAREeHjCIk2lyzLaG9vx/DwMAoLC7ngxBJaW1sRGBiI9PR0n8UwOzuL7u5u7Nmzx2cx0GIjIyNoaWlhfzEiIiI/wh5iRLQtSZKEw4cPo7u7GyMjIwAAnU6HiooKNDQ0YGBgwMcREm0OIQQGBgZw4sQJBAQEoLKyksmwJdjtdgwNDSEtLc2ncbCH2NYUHx+PyspKAOwvRkRE5C9YIUZE25rL5UJNTQ3y8/PdSQBZlnHx4kVotVrk5eXxSj/tWPOLSkRFRSE3N5c99FZw5coVJCYm+nxRAYPBgI6ODuzbt8+ncdDy5vuLmc1mFBcXs78YERHRDsUKMSLa1pRKJY4cOYKmpiZMT08DABQKBQ4ePAhJknD+/HnIsuzjKIk8y2q14vz582htbcXBgwdRWFjIZNgKTCYTjEajz5NhwNznE69Fbm0ajQZ79+5FcXEx6uvrceXKFTgcDl+HRURERB7GCjEi2hFsNhvOnDmDAwcOQKfTubcPDAygq6sLpaWlCAgI8GGERBvncrnQ1taGsbExFBUVISoqytchbQt1dXXIzc1FeHi4r0OB2WxGc3MzDh486OtQaJXYX4yIiGhnYoUYEe0IAQEBKCsrw4ULF2Aymdzbk5OTUVxcjJqaGuj1eh9GSLR+Qgj09fWhuroaWq0Wx48fZzJslaanpyFJ0pZIhgHsIbYdzfcXE0KwvxgREdEOwgoxItpRTCYT6urqcOTIEQQFBbm3WywW1NXVIS8vD/Hx8T6MkGhtpqam0NDQgNjYWOTk5ECpVPo6pG1DCIHTp0/jwIEDCA4O9nU4AOamu9bX1+Pw4cO+DoXWwW63o6mpCRaLBSUlJdBqtb4OiYiIiNaJCTEi2nH0ej0uXryI8vLyBdMknU4nzp8/j9jYWGRlZfkwQqKbs1gsqK+vh0KhQFFR0YIEL63O0NAQJiYmUFxc7OtQ3Ox2Oy5duoSysjJfh0IboNfr0dDQAK1Wi4KCAqjVal+HRERERGvEKZNEtOPodDrs27cPtbW1Cxohq1QqlJWVwWw24/Lly5y2RFuS0+lEU1MTzp8/j5ycHBw6dIjJsHUQQqC1tRV5eXm+DmUBTpncGXQ6HSoqKhAfH4/Tp0+jq6uLrysREdE2w4QYEe1I4eHhKCoqQm1tLZxOp3u7JEkoLi5GZGQkampquHIYbRlCCPT09ODkyZMIDw/HsWPHEBER4euwtq3u7m4kJydvucodJsR2lvn+YrIs48SJExgbG/N1SERERLRKTIgR0Y4VHR2N3Nxc1NbWQpblBbelp6cjNzcXp0+fXtCEn8gXJiYmcPLkSVitVlRVVSE5OZkr2W2A0+lEd3f3lpwarVAoFn0e0famUCiQnZ2NiooKDAwM4MyZMzAajb4Oi4iIiG6CPcSIaMcbHBxET08PysrKoFAsvA5gMplw7tw5FBcXIzo62kcRkr8ymUxoaGiAWq1GUVHRgp53tH7Nzc3Q6XRISUnxdSiLzDf6P3bsmK9DIS9hfzEiIqLtgRViRLTjJSUlITk5GRcuXFg0VSkkJARHjx7FtWvX0Nvb66MIyd84nU7U19fj8uXLyM/Px4EDB5gM8xCr1Yrx8XEkJyf7OpRl8VrkzjbfXywuLo79xYiIiLYwJsSIyC+kpaUhKipqyWb6arUa5eXlmJiYQGNjI7+4kNcIIXD9+nWcOnUKMTExOHr0KMLCwnwd1o7S3NyMgoKCLTvldKvGRZ6XkJDA/mJERERbGBNiROQ3srKyEBISgsbGxkW3KRQK7N+/H4GBgairq4PL5fJBhLSTjY2N4cSJE3A6naisrERiYqKvQ9pxDAYDrFYrYmJifB0KEQD2FyMiItrK2EOMiPxOY2MjVCoV8vLylrx9eHgYra2tKC0tRVBQ0CZHRzuNwWBAQ0MDgoKCUFhYCI1G4+uQdqwzZ86gqKgIOp3O16Gs6OTJkzh+/LivwyAf0Ov1qK+vh06nQ35+PvuLERER+RArxIjI7xQWFsJisaCzs3PJ2xMSErBv3z7U1tZiZmZmc4OjHcNut+PKlSuor69HUVER9u3bx2SYF01MTCAgIGDLJ8PIv+l0Ohw9ehSxsbE4deoU+4sRERH5EBNiROR3JEnC3r17MTU1hZ6eniX3CQsLQ0VFBa5evYrBwcHNDZC2NVmW0dnZiZqaGiQkJKCiooJJGi8TQqCpqQmFhYW+DoVoVRISElBVVQWXy8X+YkRERD7ChBgR+SVJknDw4EEMDQ1hYGBgyX0CAgJw9OhR9Pf3o7W1lVfx6aZGRkZQXV0NAKiqqkJ8fLxvA/ITg4ODiI6ORmBgoK9DIVo1hUKB3bt3o7y8HP39/aitrYXJZPJ1WERERH6DCTEi8luSJKG0tBTd3d0YGRlZch+lUonDhw9DlmVcvHgRsixvcpS0Hej1etTU1GBkZARHjx5FVlYWVxPcJLIso62tDbm5ub4OhWhdAgICsH//fhQWFuLy5cuor6+Hw+HwdVhEREQ7HpvqE5Hfc7lcqKmpQUFBAaKjo5fdr7+/H9evX0dZWRl7QREAwGazoampCVarFSUlJdBqtb4Oye90dHRAoVAgMzPT16GsGpvq00qGhoZw7do1ZGRkICMjg8l1IiIiL2FCjIgIgMPhQE1NDfbs2YOIiIhl95uamsKVK1dw6NAhhIaGbmKEtJXIsoyOjg4MDg6isLAQsbGxvg7JLzkcDpw+fRpVVVXbKmnAhBjdzPxnzNDQEAoLCxETE+PrkIh2LiGAkRGgqwswGABJAnQ6IDMTiI2d+5mIdiQmxIiI/spms+HMmTM4cODAik3QzWYz6urqUFBQgLi4uE2MkHxNCIGhoSG0trayemMLaGhoQHR0NBITE30dypowIUarNV+FarPZUFJSgpCQEF+HRLRz9PcDL74I/P73wOgoYDIBTufcbWo1EBwMJCQAd98N3H47kJTk23iJyOOYECMiuoHFYkFtbS1KS0tX/OLhdDpx7tw5xMfHb6upWrR+MzMzaGhoQHh4OPLz86FSqXwdkl+zWCw4f/48jh07tu2SkkyI0VrNzs6ioaEBOp0OBQUF/Pwh2gijEfjhD4Ff/QqYngaCgoDQUCAkZC4RJsRcYsxkAvR6wGoFIiOBd78beO975xJlRLQjMCFGRPQaJpMJdXV1OHLkCIKCgpbdTwiB+vp6CCGwZ8+ebfelnFbHarWisbERTqcTJSUlCOaJ8JZw/vx5ZGVlITIy0tehrBkTYrRe7C9GtEGtrcDnPw80NgJhYUB0NKC4yTpzsgyMj88lx/buBb76VSAra3PiJSKv4iqTRESvERISgoMHD+Ls2bOw2WzL7idJEvbs2YOwsDCcOXMGzvkye9oRXC4Xrl27hrq6OmRkZKCsrIzJsC1idnYWLpdrWybDiDYiMTERVVVVcDgcqK6uxvj4uK9DIto+mpuBhx4CGhqAtLS5/mA3S4YBc/vExQGpqcDly3NjtLd7P14i8jpWiBERLWN6ehpXr15FRUUF1Gr1ivuOjY2hqakJpaWlTJpsc0IIDAwMoL29HVlZWUhNTWUVxhYzvwDGdl3VkxVi5AnsL0a0BmNjwIMPAtevA7t2rS4RthSXa26MnBzgySfnplIS0bbFCjEiomVERESgqKgItbW1cLlcK+4bGxvrriqbnJzcpAjJ06anp3Hq1CnMzs6isrISaWlpTIZtMWNjYwgODt62yTAiTwkICMD+/ftRUFCAS5cuoaGhgZXKREsRAnj0UaCzE0hPX38yDACUyrkxWluB731vbmwi2raYECMiWkF0dDRycnJQW1sLWZZX3Dc0NBRHjx5Fc3Mz+vr6NilC8gSLxYJz586hvb0dhw4dQmFhIZRKpa/DotcQQqC5uRkFBQW+DoVoywgLC8OxY8cQHR2NkydPoru7G5wAQnSDM2eAF16Ym/boiQUp1GogJgZ47jng4sWNj0dEPsOEGBHRTcTHx2PXrl2oq6u76ZcMjUaDiooKjI6Oorm5mV9KtjiXy4Xm5macP38e2dnZOHz48IoLKZBv9fX1IT4+HgEBAb4OhWjLme8vZrfb2V+M6Ea/+Q1gs8010feUiAjAbAaef95zYxLRpmNCjIhoFZKSkpCUlITz58/fNMmlUChw4MABqFQqnDt37qbTLWnzCSHQ29uL6upq6HQ6HDt2jA3atziXy4XOzk7s3r3b16EQbVkKhQI5OTk4cuQIent7cfbsWZhMJl+HReQ7/f1ATY13en1FRAB//vNcfzIi2paYECMiWqW0tDRERUXhypUrN02KSZKEnJwcpKam4vTp07BarZsUJd3M5OQkTp48CbPZjMrKSqSkpLBP2DbQ0dGBzMxMTmUlWoWAgAAcOHAA+fn57C9G/q25GdDrPVsdNi88HJidBVpaPD82EW0KJsSIiNYgKysLQUFBaGxsXNX+iYmJ2LNnD86cOYPZ2VkvR0crMZvNqKurw/Xr11FaWoq8vDwmV7YJu92OoaEhpKWl+ToUom0lLCwMR48eRVRUFPuLkX/q6gIkaa4ZvqepVHNN9Ts7PT82EW0KJsSIiNYoLy8PAHDt2rVV7R8eHo7y8nJcuXIFQ0ND3gyNluB0OtHQ0ICLFy8iNzcXBw8eRGBgoK/DojVoaWlBfn7+jqrkY1KCNoskSUhKSkJVVRVsNhv7i5F/GRlZ0+5r/mQWglMmibYxJsSIiNahqKgIFosFnau8KhgYGIijR4+it7cX7e3tXo6OgLmEQ3d3N06ePImoqCgcPXoU4eHhvg6L1shkMsFgMCA+Pt7XoXiMJElMiNGmUygUyM3NZX8x8i+rnCrscDgwq9djaHAQRqNx9eMLAbBXLNG2xYQYEdE6SJKEvXv3YnJyEr29vau6j1KpRGlpKex2Oy5evAhZlr0cpf8aHx9HdXU17HY7qqqqkJSUtKOqi/xJY2MjioqKfB2GRykUCibEyGfm+4vl5eWxvxjtfMHBc0mrJbhkGUajERMTEzCZTNCo1QgICIDNbl/9+JIEsOqcaNtiQoyIaJ0kScKhQ4cwODiIgYGBVd+nsLAQMTExqKmpgX0tJ110UyaTCbW1tejr68ORI0eQk5MDhYK/6rar6elpSJK04yr7WCFGW0F4eDj7i9HOl54+l7T6KyEEzBYLJqemMDszA6VKhaioKISHh8NisSAsPBwQYnX/FoSYG5v9LYm2LZWvAyAi2s4kSUJpaSnOnDkDlUq16mldaWlpCAkJwenTp3H48GFotVovR7qzORwONDc3w2AwoKSkBDqdztchkQc0NjbiwIEDvg7D4yRJYoUobQnz/cUSEhLQ3t6O6upqFBUVITo62tehEXlGZiaEUgm7wQCz0wmXLCMoMBAR4eELLpjZbDYolEqoVaq5KjGb7eb9Rq1WQKMBsrK8/CCIyFt42ZyIaIMUCgWOHDmCtrY2TExMrPp+0dHRKC0txfnz59ngeJ2EEOjq6sLp06cRFxeHo0ePMhm2QwwPDyMsLAzBwcG+DsXjOGWStpob+4v19PTg7NmzMJvNvg6LaEP0ej0aFQpMBwTANTaG0NBQREdFISQkZEEyTAAwGAwIDQ0FMNf31Wq13vwAExNAcjJQUOClR0BE3saEGBGRByiVShw5cgSNjY2Ynp5e9f1CQkJw9OhRtLW1obu724sR7jyjo6M4ceIEZFlGZWUlEhISfB0SeYgQAteuXXOv6LrTcMokbVU39he7ePEiGhsb2V+MthWr1Yq2tjZUV1ejra0NCVlZiHjwQQS7XFAt00LBbDYjMDAQyr/erlKp4HQ6V15x0uUCLBbg3nuBgADPPxAi2hSS4BkZEZHH2Gw21NTU4ODBg2uqVBJC4OrVq1AoFCguLmYD+BUYDAbU19cjJCQEBQUF0Gg0vg6JPOz69etwOp3YvXu3r0PxivPnz6OoqAhBQUG+DoVoWUIIDA0NobW1FZmZmUhLS+PvJtqSXC4XBgcH0dvbC0mSkJaWhsTERCiVyrkdBgeBd78bmJkBUlIW3FcWApOTk4iOilrw/p6dnUVwcDDUavXSB+3tBeLigKeeAmJjvfTIiMjbmBAjIvIwi8WC2tpalJaWIiQkZE337ezsxOjoKA4fPgyVim0eb2S329HU1ASLxYLi4mL31AbaWVwuF6qrq1FVVbVjF0S4cOECCgoKduR0UNp5ZFlGe3s7RkZGUFhYyP5itCUIITA+Po6enh6YzWYkJSUhNTUVActVaz39NPClLwHR0cANFyz1ej00Gs2ifmE2mw12u33pc42Zmbk/X/86cPfdHntMRLT5mBAjIvICk8mEuro6HDlyZM1VIKOjo2hubkZZWRkrSDD3Zayrqwv9/f0oKChAXFycr0MiL2ppaYFWq0VqaqqvQ/GaS5cuIScnh4tp0LZitVrR2NgIl8uF4uJiJnTJJwwGA7q7uzExMYHo6GhkZGSs7gKZywX8678Czz8PxMcDoaFwulyYnZlBZFQUXlv7KITA5NQUoqOiFt6g1wOjo8A73jGXYGPVJNG2xoQYkY8M6AfQOtGKzqlOzFhnIIRAsDoYuyJ2ISsyCznROVBIO7M6wl/o9XpcvHgR5eXly1+xXOG+Fy5cwN69exEZGemlCLe+4eFhXLt2DWlpadi1axen6+xwNpsNZ8+exfHjx3f0a3358mVkZ2ezypG2pZmZGTQ0NCAiIgJ5eXmsZiavs9ls6Ovrw+DgIIKDg5GRkYHo6Oi1/56wWIAvfAH44x+BkBBMaTQIDQ1ddlrk1PQ0wsLC5nqLCQEMD8+tLHnPPXPjsHcY0bbHhBjRJpKFjJM9J/Fc63M4O3AWRrsRAsLdXHm+0XKwOhi7o3bjLXlvwe3Zt0OrYRXBdjU9PY2rV6+ioqJi+T4Uy7DZbKirq0NmZiaSk5O9FOHWNDs7i4aGBuh0OuTn56/5uaPt6dKlS0hNTUVMTIyvQ/GqK1euIDMzkyui0rYlhMDg4CDa2trYX4y8wuVyYWhoCL29vQCA1NRUJCUl/a0v2HrZ7cBPfgLH44/DOT6OoORkIDwcWGKKvtlsBmQZwTYbMDkJREUBH/zgXD8ynpcQ7QhMiBFtkgH9AL515lv4S89f4JSdiAqKgi5AB6Vi4S92WcgwO8wYN41DFjLyY/LxmfLP4GDSQR9FThs1MTGB5uZmVFRUrPlETpZlXLx4EVqtFnl5eTv+C4fNZkNjYyMcDgeKi4vX3IONtq/5xRIqKip8HYrX1dfXIy0tDeHh4b4OhWhDZFlGW1sbRkdH2V+MNkwIgYmJCfT09MBkMiExMRGpqamL+nt54jgXnnwS+65ehaqubm4apCQBwcF/S3Q5HJBNJlgtFgTHxwPHjgHvfz+Qk+PRWIjIt5gQI9oEtf21+PxfPo8BwwASQxNXXfFld9nRP9uPEHUI/uHgP+DBPQ/u+ITITjUyMoKOjg6Ul5evuVG4EAKtra3Q6/U4ePDgjmw0Pt+0eXh4GIWFhTu+QogWq62tRWFhoV9UTTU0NCAlJQURERG+DoXII9hfjDbCYDCgp6cH4+PjiIqKQkZGhld/F/T09MBisSAvLw/o6QGqq4Fr14DGxrnkGACEhQFFRWiVJOz+8IehSEvzWjxE5DtMiBF52dn+s/jkK5/EjHUG6eHpa+4LJoTAmGkMFqcFDx96GO/f934mxbapgYEB9PX1oaysbF2v4cDAADo7O1FWVrbmnmRb1Y3Tbnbt2oX09HS+v/3QxMQEuru7cfCgf1TCNjY2Iikpya/7A9LONDMzg/r6ekRFRSE3N5f9xWhZdrsdfX19GBgYQFBQEDIyMhATE+P1cwCn04mTJ0+isrJy6ap9l2uuWuyvFx9bWloQFRXFBX2IdigmxIi8aNgwjPueuw9D+iFkRGRs6Jf8qHEULtmF/7ztP3E8/bgHo6TN1NPTg7GxMRw8eHBd74fp6WlcvnwZBw8e3PaVNGzMTMBcUvTkyZMoLS31+LSYraq5uRlxcXGcXkY70o0XOrKyspCamsoLHQRgrhp8vi+YLMtITU1FcnLyxvuCrUFTUxPCw8NX3Zt1dnYWXV1d2Ldvn5cjIyJf4LcPIi8RQuA/6/4TfTN9yIzM3PDJYJw2Dtenr+PbZ7+N4rhiRARxqs12lJ6eDqfTiStXrmDv3r1rfl9ERETgyJEjqKurQ15eHuLj470UqfdYrVY0NDRAlmUcOHCAU2v83ODgIKKjo/0mGQb8bQEVop1IkiQkJycjISEB7e3tOHnyJIqKihAVFeXr0MgHhBCYmppCd3c3DAYDEhMTsX//fp985pvNZkxOTqKgoGDV99HpdJidnfViVETkS6wQI/KScwPn8KE/fAhhgWHQBXimksfhcqBnpgf/XPbP+OD+D3pkTPKNa9euwel0oqioaF33d7lcOHfuHGJiYpCdne3h6LzD5XKhra0NY2Nj/HJEAOaqBU6cOIHjx4/7VYXgtWvXEBUVhdjYWF+HQuR1N14EYX8x/2EymdDd3Y2xsTFERkYiIyMDYWFhPo2prq4OOTk5a+7feOnSJWRnZ2/7ynwiWmzndWYm2iJ+1/Y7WJ1WjyXDAECtVCNIHYTfXvstrE6rx8alzZeXl+dulr8eSqUSZWVlsFgsuHz58pauNhFCoK+vD9XV1dBqtTh+/DiTYQQAuH79OtLS0vwqGQawQoz8S2BgIA4dOoScnBxcuHABTU1NcLlcvg6LvMBut6OrqwsnT55EY2MjYmJiUFVVhT179vg8GTY5OQmFQrGuxUwSExMxNDTkhaiIyNeYECPygmnLNE70nEB4YLjHx44JjkG/vh/nB897fGzaXEVFRTCZTOjs7FzX/SVJQnFxMSIjI1FTUwOHw+HhCDduamoKJ0+ehMFgQGVlJXvJkJvT6URvby927drl61A2nSRJkGXZ12EQbaqIiAgcO3YM4eHhqK6uRm9vLxPDO4AsyxgcHERtbS3q6uqgVCpRXl6O0tJSxMXFbYnf+UIINDY2rrsqPzY2FqOjox6Oioi2Av+6JEu0STqmOmCwG5CgTfD42AGqALiEC+2T7TiWdszj49PmkSQJ+/btw/nz56FWq5G2ziW909PTERISgtOnT+Pw4cMICQnxcKRrZ7FY0NDQAEmScPjwYQQFBfk6JNpirl27htzcXCgU/ndtTqFQMBFAfon9xXaG+b5gPT090Ov1SEhIwN69e7fs7/r+/n7ExMSsOz6lUgmlUgm73Q6NRuPh6IjIl5gQI/KCrqkuOFwOaJTe+aUpQULrxPqm2tHWIkkSDh48iLNnz0KlUiEpKWld48TExODw4cOoq6tDSUmJz1avczqdaG1txeTkJIqLi9c1NYF2PovFgqmpKRQWFvo6FJ/glEnyd0qlEnl5ecjIyEBDQwM6OjpQUlKyZRMqNMdkMqGnpwejo6OIiIhAZmYmwsPDfR3WilwuFzo6OlBZWbmhcRISEjA8PLzui5dEtDUxIUbkBbO2WUiStKYycZPJBJVSBUkhQSEpICkk9xivHUetVGPCPOHpsMlHFAoFysrKcObMGahUKsTFxa1rnJCQEBw7dgx1dXVITU3d1JM2IQR6e3vR1dWFnJwcFBQUbIlpErQ1NTU1obCw0G/fI0yIEc2Z7y82PT2N8+fPIyoqCnl5eVAqlb4Ojf7K4XCgv78f/f39CAgIQHp6OvLz87fN53dbWxuys7M3/J5KTEzE1atXmRAj2mGYECPyAiEEJKztRMFqtcJuswMSoFKpoFLO/fOUxeI+M0arEcMjwzh9+jRUKhXUavXcff7658afl/v7djmR8RfzSbGamhoolcp1V3ip1WqUl5fjypUr0Ov1m5J0mJiYQGNjIxISElBVVeWXU+Bo9WZnZ+F0Ov16ipRCoWAPMaIbzPcXGxgYQHV1NbKysthz0odkWcbIyAh6e3vhcDiQkpKC8vLybbcAitVqxdjYGPLy8jY8VlBQEGw229w5Pt+XRDvG9vpUI9omdAE6yEJe0y/N+S+HskuG1WaFzWqDS3ZBo9YgMDAQao3aPdbM1Azy0/NRXl4Op9MJp9MJh8Ox6O8WiwV6vX7JfearE15bqaBUKtecXFOr1VAqlTxB2CCVSoXy8nLU1NRg7969656GoFAosH//fnR0dKCurg6HDh3yytV2k8mEhoYGqNVqlJWVITAw0OPHoJ2nsbERe/bs8XUYPsUKMaLFJElCSkoKEhMT0dbWxv5im0wIgZmZGXR3d2NmZgYJCQkoKSlBcHCwr0Nbt4aGBhQVFXns/DQ6OhoTExOIiYnxyHhE5HtMiBF5wa6IXVApVHDIa+8jplAqEBwcPHcCIgC7ww6r1QqDwQBJISEwIBAu2YXc6Llm1BqNxqMNPmVZXjK5Nv93m8225Pb5JdRv/JI3/6VvPsm21ko2f0yyqdVqHDlyBGfOnMGBAweg0+nWPVZ2dja0Wi1OnTqF0tJSj/VmcTqdaGlpwczMDEpKSny+lDptH2NjYwgODoZWq/V1KD7FhBjR8pRKJfLz87Fr1y40NDSgs7MTxcXF7C/mJWazGT09PRgZGUF4eDgyMjIQHh6+7c+/pqenIcuyRxOqiYmJ6OvrY0KMaAdhQozIC7KjshGqCYXepkd08Aaam0tYkPCSXTIMFgPsNjuMfUbUq+uRmJiIqKgoj01TUygUCAgIQEBAgEfGE0JAluUVK9lsNtuS210u17KVbDcm1tZSyaZQKLb8SV5AQADKyspQW1uL0tLSDa0amZCQgODgYNTW1mL//v0ban4rhEB3dzd6enqQm5uL4uLidY9F/kcIgebmZhw5csTXoficQqGAw+HwdRhEW9pr+4tFR0cjNzeX/cU8wOl0uvuCza9yvZNW/RVCoKGhAQcPHvTouBEREaivr/fomETkW5LgJUoir/jUy5/CH9r/gKyoLI+OO6gfRHRwNH7z9t/AZrRheHgYExMTUKvVSEhIQEJCwo6/ijqfZFupkm25v8/37XltJRuAJSvZVvN3byXZjEYjzp07h/Ly8g1PR7TZbDh79iyys7PXtZLl2NgYmpqakJycjKysrB1z0kybp6+vD0ajEfn5+b4OxecGBgZgs9mQmZnp61CItgUhBAYGBtDe3o7s7GykpKRs+YtbW40QAqOjo+ju7obdbkdKSgpSUlKgVqt9HZrHDQwMYHp6GkVFRR4f+/z58ygoKNjQxUoi2jqYECPyktO9p/GRFz6CqOAoaDWemR7klJ24Pn0dDx96GB859JEFt9lsNoyMjGBoaAhWqxWRkZFISEhAdHQ0kxerIISAy+VadXLttZVs826sZJMkaUOVbACg1+tx8eJFlJeXb7hqz+Vy4cKFCwgPD0dOTs6qvkwYjUbU19cjKCgIhYWFHp2eS/5DlmWcOHEClZWVrO4AMDg4CIvFgqwsz14wIdrpXC4X2traMDY2huLiYkRGRvo6pC1vvi/Y9PQ04uLikJ6evqOTOfO/b44fP+6VRQAGBgZgNpuxe/duj49NRJuPCTEiL3HJLnzspY/hleuvICsyCwpp40mpnukeJOmS8LN7f4aYkOX7FwghMDU1taB6LD4+HomJiTu+emwrEUIsSKCtp5INACwWC0ZGRpCamrqu5NqNlWxCCFy7dg1GoxEHDhxYNllqt9vR3NwMk8mE4uLiDfUyI2pra4NGo0FGRoavQ9kShoaGYDKZkJ2d7etQiLYli8WCxsZGCCHYX2wJFosFPT09GB4eRlhYGDIyMhAREeEXVXWtra0ICAjw2u8bp9OJ2tpaHDt2zCvjE9HmYg8xIi9RKpT4dPmn0TTWhN6ZXqSHp2/oRGTCPAGlQol/Kv2nFZNhwFxlUlRUlLuR6Hz1WH19PSwWCyIiIpCYmMjqMS+TJAlqtdoj0xHGx8fR0tKCI0eOuBNty/VjMxqNSybabkyy6fV6/OxnP0NKSsqihQympqYwPT2N9PR0pKSkwGAwwGKxLLkwAt8/dDN2ux2Dg4OoqqrydShbhkKhWPDvkYjWJigoCIcOHcLU1BT7i/2V0+nEwMAA+vr6oFKpkJaWhpycHL/6PW2zzbUSqays9Nox5qvOnE6nVyrQiGhzsUKMyMv+0v0XfPbPn4XZYUZaWNq6kmIT5gnobXq8f+/78fHSj28osSaEwPT0NIaHhzE+Pg6VSuXuPbadl9b2ByMjI+js7MSRI0c8coI7NTWFK1eu4MCBAwgKCsLg4CBaW1sRFxeHxMTEVU0hXe5XiEKhWHclmz9cwfYnV69eRXx8POLj430dypYxOjqK6elp5Obm+joUom3Pn/uLCSEwNjaG7u5u2Gw2JCcnIyUlxW/bG1y8eBFpaWleXwWyo6MDQUFBSE5O9upxiMj7mBAj2gSvXn8VX67+MsbN40jSJSFYvbrEk1N2om+2DxqFBg/seQAfPfRRKBWevfppt9vdvcfmq8cSEhIQExPjV1cVt4uBgQH09/ejtLTUIyf8ZrMZJ0+ehCRJiI+PR0FBgUcq2m62suhKf18pybbW5JparYZSqfSbL0dbjdlsxsWLFzm15DXGxsYwOTmJvLw8X4dCtGP4U3+x2dlZdHd3Y2pqyi/6gq3G7Ozspq1kbDKZ0NTUhMOHD3v9WETkXazzJNoEt+y6BWlhaXik5hGcGzwHIQSiQ6IRqgld9EVdCAGr04px8zhsThsyIzPxybJPojK90itf6jUaDVJTU5GamgohBGZmZjA0NITW1lYolUp37zFWj20NycnJcDgcuHDhAg4ePLih94TNZsO1a9eg1WrhcDgQGhrqsdWmFAoFNBqNR69S32xlUZvNtuR2l8u1bJJtqZVFV5NsY5JtdRobG1FcXOzrMLYcSZI4ZZLIw5RKJfLz85GRkYGGhgYA2FH9xaxWq7svWGhoKDIyMlBSUsLfRX/V0NCAffv2bcqxQkJCYDabIYTg80+0zbFCjGgTOVwOvNT5Ep699iwaRxthdpghMPfLVIIEWcx9QdIoNUgLT8Obc9+M/y/3/0NkkG+ucs5Xjw0PD8NsNiM8PByJiYmsHtsCOjo6YDQasWfPnjWfjMmyjI6ODgwODqKwsBCxsbEQQqC+vh5CiHWNuR0JIdyVbMutILrc31dKsm1kZdGd9LxPT0+jvb2dV9CXMDk5ieHhYRQWFvo6FKIda2pqCo2Njdu6v5jL5XL3BVMoFEhLS0NiYiLPwV5jaGgI4+PjKCkp2bRj1tfXIyUlZUdXIhL5AybEiHxACIHWiVa0T7ajY6oDE+YJyEKGLkCHzIhMZEVmoSS+BBrl1ukBcWP12MTEBKvHtoCWlhbIsrzqL9VCCHf1X0ZGBjIyMhYlYK5fv46hoSGUlpayWew6CCHcvdfWOmXU5XItO+5GVhb1VZLt1KlTOHDgAD8fljA1NYXBwUEUFRX5OhSiHU0Igf7+fnR0dGyb/mLzfcF6enpgsViQnJyM1NRUv+0LdjOyLOPEiRM4duyYx6rcV2N8fBwjIyP8HCfa5pgQI6J1YfXY1lBfX4+AgICbNueemZlBQ0MDwsPDkZ+fv2Kya2xsDE1NTSgtLWUyYwu4Mcm2muTaayvZliJJ0oYq2W5mZGQEIyMj2LNnj4efjZ1henoafX19m1rNQOTPXC4XWltbMTExgeLiYkRERPg6pEX0ej26u7sxOTmJ2NhYpKenQ6vV+jqsLa+9vR1KpRKZmZmbelwhBE6ePOnVFS2JyPuYECOiDZuvHptfuXK+eiwhIcHvm7x6mxACly9fRnh4+JIng1arFY2NjXA6nSguLl7162EwGHD+/Hns2bMHUVFRng6bfEwIsSCBtpZKtuV6X80n2ZRKJa5fv47c3FwEBgauupLNn8w3xF5twlAIAfOEGXaDHZCAwPBABEXsjL5IRJvJYrFsqf5iNpsNvb29GBoaQkhICDIyMhAVFbXlq9i2CrvdjpqaGlRVVfnkOTt79iz27Nnj8/cREa0fE2JE5HF2ux2jo6MYGhpyV4/Nr1y5HXt4bHVCCJw/fx7x8fFIS0sDMHc1vL29HaOjoygsLER0dPSax7Xb7airq0N6ejpSU1M9HTbtMPNJtq6uLlgsFqSlpa060bZSkm01CbWlft7KSTa9Xo+uri7s3bt32X1cdhd6T/ei+y/dGK0fhWncBNk59zwp1UqEJoYifm88Mm/NROLBRCiUW/fxEm01U1NTaGhoQGxsLHJycjb13MTlcmFwcBC9vb2QJMndF4znR2t3+fJlJCUlIS4uzifH7+vrg8Ph2PTqNCLyHCbEiMirhBCYnZ11NzxVKBTu3mOsHvMcWZZx9uxZpKWlQQiB9vZ2ZGVlITU1dUNXTWVZxqVLlxAUFISCggJetaYVuVwuVFdXo6qqyiMJqflFD242NXSpvy93eqNQKNbdk81T73+DwYCOjo4lV0QTQqD99+249MQlzHTPQHbJUAeroQnRQKGae05ddhccZgccFgdUASpE50bj0EOHkFrBxDXRat3YX2z37t1ITk722u84IQQmJibQ3d0Ns9mMpKQkpKamIiAgwCvH8wd6vR6NjY0oLy/3WQx2ux3nz59HRUWFz2Igoo1hQoyINpXD4cDIyAiGhoZgMpkW9B7j1dGNmZiYwAsvvIDMzEyUlpZ67PmcT7BNT0/j4MGDfJ1oWS0tLdBqtVu6ovDGJNtqp4nO/32lJNtakms2mw3Xr1/HwYMHF3wBN42bUPNIDbpe7oIECdp4LVSBKy9uYTfZYRo1QRmgRP5b83H4Y4ehCWHzbaLV8mZ/MYPBgO7ubkxMTCA6OhoZGRkIDQ312Pj+rKamBiUlJT5/Pk+ePImKigqeGxFtU0yIEZHPzFePDQ8PY2xszF09lpCQwEaya3BjT5T8/HxcunQJRUVFHu/9NTQ0hPb2dpSWliIwMNCjY9P2Z7PZUFtbi8rKSr+rJFzryqJmsxmDg4NISEhwJ9lskzZ0fq8Txg4jNFEaqEPUkCQJkkKCJElQSAr3zzf+ff4264wV1ikrMqoycOu3bkVAKCtPiNZi/nepJEkoLi5e9+85m82Gvr4+DA4OIjg4GBkZGYiOjva7z0Vvml/UaaVp55ultbUVYWFhSEhI8HUoRLQOTIgR0ZbhcDjcvceMRqO791hsbCyvvC3hxqvaRUVFiIyMBPC3JrP79u1DeHi4R485MzODS5cu4cCBAwgLC/Po2LS9Xb58GcnJyYiNjfV1KFuexWJBY2MjDh06BACwGWz4w4f+gOFLwwhPD4dCowAEIAsZQhYQYu7PjT8vuk2W4bQ4YR2zIvJQJDL/MdM9xXKeUqlc8zTR+ZVF+WWe/MXk5CQaGxvX1F/M5XJhaGgIvb29AIDU1FQkJSXx3MULhBA4ceIEKioqoNH4vhpWr9ejvb0dBw4c8HUoRLQOK9fhExFtIrVajeTkZCQnJ0MIAb1e765KUigUiIuLQ2Jiot9Xjwkh0NfXh87OTuzevRv5+fkLvqxqNBqUl5ejpqYGhw4d8uh0gvDwcJSXl6Ourg67d+9GYmKix8am7ctoNMJsNjMZtkqSJC2YfnnpiUsYvjyMsLQwKDV//QItAUpJCaylFVsEYNfZYWw1ImY8BoXvLHTfJIS4aSWbxWJZcrvL5Vr2kOvtx8YkG21VUVFROH78OPr6+lBdXb1sfzEhBCYnJ9Hd3Q2TyYTExEQcOHCAFdRe1tXVhZSUlC2RDAMAnU4Hg8EAIQQ/04i2IVaIEdG28NrqsbCwMCQmJvpd9dj8leu4uDjs3r17xcdusVhQW1uLsrIyBAcHezQOl8uF8+fPIyoqCrt37/bo2LT9nD17FgUFBdDpdL4OZVuw2Wy4fPkyysrKMHxlGL//wO+hVCsRHO2Zf6f6AT00Og3e8n9vgS7Je6/JjUm21fRgu/Hn5ZJskiRtqJKNyJNcLheuXbuGyclJd38xo9GI7u5ujI+PIyoqChkZGfzs2yQOhwOnT59GZWXllvr3fvnyZWRmZrJynmgbYkKMiLadG6vHxsfHIUkS4uLikJCQ4PPmqt5iNpvR0NAApVKJoqKiVV+BNhqNOHfuHMrLyz1+1VoIgebmZlitVuzbt29LnZzS5pmcnHQ3iKfVcTgcuHDhAo4cOYI//+ufce031xCZFemx6gIhC0x1TaH0n0px4EPbaxqPEGJBAm0tix/IsrzkmJIkbaiSjWh2dhanT5/G7Ows0tLSsHv3bsTExLAiaJNdvXoV8fHxiI+PX/8gQgC2ScDYNfd/yIAyGNBmAMEpgGLtE6hGR0cxOTmJ/Pz89cdFRD7BhBgRbXtOp9PdYNVgMLibm8bFxW376jGn04lr165henoaxcXF6+oJNjs7i0uXLnmt30ZfXx96enpQWlq6ZaYw0OYQQuDUqVM4dOgQgoKCfB3OtuF0OnHu3DkU7yrGr+79FSRJ8lh12Dz9gB4hcSF453PvvOlKlTvdfJJtuWq1lf6+UpJtNQm1pX5mkm17kGXZ3RdMlmWkpqYiODgYzc3Na+ovRp5hNBpx5coVHD16dH0D2KaA4T8BA88Dph7AaQJkx9xtkjSXFNNEAPG3Akl3AmEFc9tXQZZlnDp1CpWVleuLjYh8hgkxItpR5qvH5leuBODuPbadqseEEOjp6cH169eRm5uLxMTEDV2JnpqaQkNDAyoqKqBSef7L8eTkJK5evYrDhw/7fY83fzI4OIipqSkUFRX5OpRtxeVyoba2FvHGeLz8zy8jPCMcCqVnkyQOiwPmCTPu/em9iCuO8+jYNPcFeLnk2s2SbcudeisUinVXsrFSyTOEEJiamkJ3dzcMBgMSExORlpa2oML6tX08l+ovRp535swZFBUVrX16quwE+p4BOv8HsI4CkgpQhwHKEEChnkt6CRfgNANOI+A0ACotEH8LkPvPQNDqPj/PnDmDAwcOICCAK/wSbSdMiBHRjuZ0Ot29x7ZL9dj4+DiampqQmJiI7Oxsj1UTjI+Po6WlBRUVFV557CaTCefOnUNRURFiYmI8Pj5tLbIso7q6GseOHfNKknUnE0Lg9OnTCKgPwIXvX0BUdpRXjjHVNYXX/7/XI+/NeR4fnzxvPsm2nkq2lZJs6+nHplQq/SrJYzKZ0NPTg9HRUURGRiIjI+Om/aCW6i9G3jE2Nob+/n7s379/bXe0TgANnwfGTgEKDRAYf/MpkUIAjlnANgaEpAGFnwNij930UN3d3QCAjIyMtcVIRD7FhBgR+Q0hBAwGA4aGhrZk9ZjJZEJ9fT0CAgJQWFjolauMw8PD6OrqwpEjR7wybcfhcODcuXNISkriSeEO19nZCQDIysrycSTbz/xUU+vvrOh8sRORmZFeOc5k5yQO/sNBHH74sFfGp63vZiuLLvd3l8u1bJJNqVTedGroUn/fakk2u92O/v5+DAwMICAgAOnp6YiLi1tzjPM9PhUKBYqLi7nKpIcJIXDixAmUl5ev7bzIOg5c+hgwdQkISgJUIWs8sAsw9QLqUKD4q0DCrSvubrPZcPHiRZSXl6/tOETkU7ykS0R+Q5Ik6HQ66HQ65ObmuqvHWltbYTAYoNPp3NVjm1nx4nA40NLSAr1ej+LiYq+uUpSQkODuX1RaWurxLydqtRrl5eW4evUq6uvrUVxcvKW+AJFnOJ1O9Pb2oqqqytehbEvz/yacFickxcr/PgQEHA4HjEYjjEYjNBoNoiKjoFarV3Usl2Pp1RzJPyiVSiiVSo9dYBFC3LSSzWazLZtsWynO9a4supbfMbIsY3h4GL29vXA6nUhNTUV5efmGfucHBwejtLQUk5OTqKurW9Uq0LR63d3dSEpKWtt7WHYA9f86lwwLTgeU6+hvKimBkAzA3Ac0fgkIigfCl28PEBAQ4F7cg30CibYPJsSIyG+pVCokJSUhKSnJXT02X0EFALGxse7qMW8kdYQQuH79Onp7e5GXl4eSkhKPH2MpKSkpcDqduHjxIg4cOODxxyZJEvbu3YvOzk7U1tbi8OHDnFK3w7S2tiInJ4cn/RukClRByIurcFwuF2w2GywWC2w2G4QQCAoKQkx0DCYmJmA0GSG7ZASHBCMwMBASlv43LEGCQsXXiDxHkiSvJNluVslmsViWrWRbzo3Va3a7HZOTk7DZbIiKikJycjK0Wi1UKhWMRuOilUXX83sxKioKx48fR19fH6qrq5GTk4OkpCReFNoAp9OJ7u7utV986fkFMH4GCEpeXzJsniQBwamAqQtofgQo/RGgXL4CMCYmBuPj44iLY99Gou2C31CIiLCweiwnJwdOpxNjY2Nob2+HwWCAVqtFYmKix6rHRkdH0dzcjJSUFFRWVm56YiEjIwNOpxNXr17Fnj17vHLCnpWVhdDQUJw6dQplZWVchXCHsFqtmJycREFBga9D2fbC0sLmKm6EDLvN7q6sma/CUSqViIyMREBggDvppVAqEBgYiABNAExmEyYmJhAUGITg4OAFnyNCCAgI6JLW2ICaaJNJkuRORnnK/EWu69evY2RkBFqtFnl5eQgKCnIn1GZnZ5dMwi2XZJtPBt6sei0kJAR79uxBT08P2tvbsWfPHkRGemda9E7X0tKC3NzctZ0jWYaBzicAZRCg8sDqvZI0l1ibvgL0PQ1kvGfZXZOSktDd3c2EGNE2woQYEdESVCoVEhMTkZiYCADu3mMbrR4zGAyor69HSEgIKioqoNFs4MrlBmVnZ6OlpQXNzc0oLCz0yjHi4uIQFBSE2tpa7N27l18KdoDGxkYUFhay6mEDhBAwm81wqB2wmC0QkwKaAA3EX/8LCAhASEjIklOuQoJDoNfrERsbi1BtKLRaLawWK6anp6FUKhGiDYFapYbT6oRKo0JkFv/Nkf9wOBzo7+9Hf38/NBoNMjIyUFJS4pHPKyHEiiuL2mw2mEymBauJqlQqvPTSSwCw5AW1G5OBa50yutMrdE0mE2ZmZta+ivHQC4BtHNB6sL+lMnBudcq+Z4G0d86tTrmEsLAwzMzMeO64ROR1TIgREa1CaGgocnJyFlSPdXR0QK/Xr6p6zG63o6mpCRaLBSUlJVuiiT8A5Ofno76+Hm1tbcjJyfHKMXQ6HSoqKlBXV4fMzEwkJyd75TjkfXq9Hg6HA1FRnl8VcaczmUwYGRnByMgIHA4H9Ho9DpQdQHtUO+xGOxwKB4KDgxGmC1vxy3tgUCDsDjusViuCAoMgQUJQUBCCgoJgd9hhNBghCxnQA2HxYYjK4WtFO5ssyxgZGUFvby8cDgdSUlI23BdsKZIkQa1Wr7p/340mJibQ1NSE2NjYBdPN55NsK/VjMxqNSybhZFleMc6bJdSW+nkrJdkaGhrW3odUdgL9v52rDpM8/FgCYwFjFzBxDoitWHIXSZKg1WphMBi2zHkeEa2Mq0wSEW3QjStXCiEQGxuLhIQE6HQ6CCHQ1dWF/v5+FBQUbMkyeiEELl26hIiICGRmZnrtOLIs4+LFi+6pK6ww2n5qamq2VEJ3K3M4HBgdHcXIyAgMBgNCQkIQHx/vXsXu97//PSIiIjD802FM1k4iJidmVeMKITA5OQkAiIqOWrJ/mNPpxFjrGOLujsPhhw4jIyODffxoRxFCYGZmBt3d3ZiZmUF8fDzS09MRHOyBKXJeIoRAb28vurq6vNpfbH7Rg+Uq2VZaXXS5r4UKhWLdlWzreYwTExPo7u7GwYMH13ZHYzdw+q2AKnRudUhPM3YCuf8EZH1w2V2Ghoag1+uRm5vr+eMTkccxIUZE5EEulwujo6MYHh7G4OAg9Ho9srOzsW/fvnVdVd4sQgicO3cOiYmJSE1N9epxWltbodfrcfDgwS11NZpWNj4+jr6+Puzfv9/XoWxJsixjcnISIyMjmJychEqlQlxcHOLj46HVagEAk5OT6Orqgs1mw+zsLO68804MXxzGH/7hD1AHqhEUubo+e1NTU1CqlNBoNAgKXHwfw5ABqiAV7v35vZjFLLq7uxEeHo7du3cjJCTEo4+baDOZzWb09PRgZGQE4eHhyMjIQHh4+La6wOJ0OnHt2jVMT0+juLgY4eHhvg7ppm62suhKf18pybZUEk2pVKKpqQklJSUICQlZVMW2YpJt+BXg0sNzq0NKXljl09gFJNwG7P/Osru4XC6cOXMGx44d8/zxicjjeLmQiMiDlEolQkJCYDabkZaWhpSUFExMTKCurg6yLLt7j+l0ui11Ai9JEg4dOoSzZ8+6+6d56zh5eXkYGBjA6dOnUVpa6rGVysh7hBBobm5GWVmZr0PZMoQQMBqNGBkZwejoKFwuFyIjI5GQkICCggJ3stfhcKCzsxP9/f2IjIxEfn4+QkNDcfLkSSgUCiQdSkL+W/JR/5N6aLQaKDU3/xIXEBgACMBkNC1aZdJhdsBhceDwxw8jIj0CEYhAeno6JiYm0NDQAJfLhd27dyMmJmZLfQYRLcfpdLr7gqnVaqSlpa290foWolKpUFRUBLPZjIaGBiiVShQVFSEwcPnVC31NoVBAo9F4tO/pciuL9vf3IyIiAk6nE5OTk0uuLLpcki3eehopFivsLiMkSYJCIUGSJEiSAgpJgqSY+/vfblPM/X+ZlXoXPxEBgLlvxV2USiUkSYLD4djSF0KJaA4rxIiIPMRms6GxsRF2u919ZfNGLpcLY2Nj7nL6kJAQJCYmIj4+fstMZ5q/spmbm4vY2FivHmt6ehqXL1/GwYMHodNxJbytrK+vD0ajEfn5+b4OxadsNpt7GqTJZEJoaKh7GuRrv/hMTU2hs7MTFosF6enpSE5OXtAk/+TJkzh+/DgAwDprxe/e/zuMNYwhPCMcSvXKSTGX7MLs7CzUKjVUapW7SsxpdWKmbwYZr8vA7f91+5LjmM1mdHR0YHJyEunp6UhLS1uyeT+RLwkhMDo6ip6eHthsNqSkpCAlJWVHJhgmJibQ2NiIhIQE7N69e9sm+jzB6XTi5MmTqKysXPPnkhAC4vpPgZZHIAenz/0sz63iK4T46+q9AuKvP8uyDLPZjMjISAQGrDIZaRkCgpOA479bcbfOzk5oNBqvVtwTkWcwIUZEtEGyLKO9vR3Dw8MoLCxETMzqegEZjUYMDQ1hdHR0S1WPOZ1O1NTUoKioyOvN0y0WC+rq6pCXl4f4+HivHovWR5ZlnDhxYl1fULY7WZYxPj6OkZERTE9PQ61WIz4+HvHx8UtOPXQ6nejt7UVfXx/CwsKQlZW1bLL3xoQYAMz2zeLFj72IiZYJhCaGQqNduRJjYnICERERmJ6aRlR0FGwzNhjHjEg9koo3/OcbEBSx8vRLl8uFnp4e9Pb2Ijo6GtnZ2QgKWt2UTSJvme8LNj09jbi4OKSnp/vFNN/N6i+21TU1NSEsLAwpKSnrG6D310DjF4GQTGCF58/lcmF6ZhohIVoEraUyz9wHhBUA5f+38m5/rf4rLS1d/dhE5BNMiBERrZMQAkNDQ2htbcWuXbuQnp6+7hPY+eqx4eFhzMzMQKvVIiEhAfHx8T65Im6321FTU4N9+/Z5vb+Jy+XCuXPnEBMTg+zsbK8ei9auvb0dKpUKu3bt8nUoXieEgF6vx/DwMMbHxyHLMqKjo5GQkICIiIhl/33PzMygo6MDJpMJaWlpSE1NvWny8LUJMWCu99epr51Cz8keKNVKaOO0y06h1Bv0CNAEwGKwwD5lR0BgAHbfvRtHPnUEgWGr/4InhHCvmqtUKpGdnY3o6OhV359ooywWC3p7ezE0NISwsDCkp6cjMjLSLxNC27G/mKdYLBacP38ex44dW/9rP3EOOPd+IDABUC7djsFmt0GvNyAiIhwq5Rqr840dQNq7gaLP33TX6upqHD9+3C/fx0TbCRNiRETrMDMzg4aGBkRERCAvL8/jUx6NRiOGh4cxMjICWZYRExODxMREhIWFbdrJldVqxZkzZ3Do0CGvryoohEBjYyOcTif27t3LE8gtYj4xWlVVtWNfE6vVipGREYyMjMBisSAsLAzx8fGIjY1d8d+1y+VCX18fent7odVqkZWVtaYvr0slxABAdspoebYFV350BfoBPSAAjVYDdbAaCvXcVCqX3QWL3gLzrBlBwUEQ4QJv/OIbkXlr5oZeJ6PRiI6ODszOziIjIwMpKSl+PX2LvMfpdGJgYAB9fX1QqVRIS0tDQkIC329/ZTabUV9f7+43tpX7i3nKuXPnsHv3bkRERKx/ENsUUH0HACUQELngJgHAZDLBbrchPDwcCmmN7zUhA6YuoPBLQPo7b7p7Q0MDEhMTeYGBaItjQoyIaA2sVisaGhogyzKKi4s3ZYl3l8uF8fFxDA0NYXZ2FsHBwe7eY96uHjObzTh79izKyso25bH29PSgv78fpaWlO7JXzHZTX1+P2NhYJCQk+DoUj5mvxhwZGcHMzAwCAwPd0yBXM2VQr9ejs7MTs7OzSE1NRVpa2roS4sslxOY5zA50n+jG9VevY6xxDNYZK2SnDABQapQIigyCLdKG13/w9TCEGxAZE4mkpKQ1x7EUp9OJ7u5u9Pf3Iy4uDpmZmX7xhZy8a74asbu7GzabDcnJyUhJSfFoo/adxl/6i01NTaGjowOHDx/e+GAXPgqM/gXQZro3CQjMzMxApVJBqw1dbQv9hexTgOwAKn4FaDNuuvvk5CQGBgZQUlKynqMR0SZhQoyIaBVcLhfa2towNja2Kb21VmIymdy9x1wul9erx4xGI86dO4fy8vJN+VI8Pj6OxsZGHD582C96x2xVZrMZFy9e3PZLxwsx90VoeHgYExMTAICYmBgkJCSs+t+My+XCwMAAenp6EBQUhKysLERGRt70fiu5WULsRrJLhmHQALvRDkhAYFggtAlaXLhwAfn5+QgICEBNTQ0qKys9+hkghMDIyIi7QXROTo5fTeEiz5idnUV3dzempqYQGxuLjIwMfravgRACPT09uH79OnJzc5GYmLijKnaFEDh58iQOHz7smT6GI68Clz4OBMYDyiA4XS7MzExDq9Wuvnn+4iABYyeQeDuw/zurvItAdXU1qqqq1ndMItoUTIgREa1ACIH+/n50dHQgOzsbKSkpW+pEdL56bL732Hz1WFxcnEevus/OzuLSpUuoqKjYlKv5JpMJdXV1KCkp4XQDH/HI9BUfMZvN7mmQdrsd4eHhiI+PR0xMzJoWBjAYDOjs7MT09DRSUlKQnp7uscrFtSTEljMwMACz2Yzdu3ejubkZ4eHhHqsSey29Xo/29naYTCbs2rULycnJW+qzkLYWq9Xq7gsWGhqK9PR0REVF8T2zAU6nEy0tLZiZmdlR/cX6+/uh1+tRUFDgmQFddqDuAWDqMmyaFBiMRoSHr6Nf2I1sE3PVYYf+B4g6sOq7nTt3DkVFRZtSYU9E68OEGBHRMqamptDY2Ijo6Gjk5uZuixX2TCaTu/fYfPVYQkICwsPDN/xFZGpqCg0NDaioqPB4z7SlOBwO1NXVuRMRtHlmZmbQ2tq6bVbIcjqdGB0dxcjICPR6PYKDgxEfH4+4uLg1VzXKsuyuBgsICHBXg3n6i7wnEmJOpxO1tbU4duwYHA6HV6rEXstut+P69esYHBxEYmIiMjMzOeWNAPytkrKvrw8KhQJpaWlITEzcsdP8fMVkMqGhoQFqtRpFRUUICFi6efx24HK5UF1d7fFVjMVUPayn3gOn04GQqEwoNvKZ6LIC5n4g6wNA3ifWdNf+/n5YrVYuGES0hTEhRkT0GhaLBQ0NDZAkCUVFRZ4p4fcBWZbdvcfmq8fmV65c7xfYsbExtLa2ory8fFMShLIs48qVK+4Tf1YXbI7Tp09j3759W3ZakxACU1NTGB4exuTkJBQKhbvXWWho6LreJyaTCZ2dnZicnERSUhIyMjK8mujxREIMAE6dOoUjR45ApVJ5vUrsRkIIDA4OoqurC8HBwcjJyYFOp/P6cWlrEUJgfHwc3d3dsFgsSE5ORmpqKpOkm2An9BdraWlBSEgI0tLSPDam0+nEhQsXkCafRMLMU5CUwUBg7PoGc1kBcx8QfQQ48Big1q7p7vMX9o4ePbq+4xOR1zEhRkT0V06nE62trZicnERxcfG2nCq2ktdWj0VHRyMxMXHN1WPDw8Po6urCkSNHNu0EvKOjAxMTEzh06NC2qNTbzuanGu7Zs8fXoSxgNBrdsTmdTkRGRiI+Ph7R0dHrfh/KsoyhoSF0d3dDpVIhKysL0dHRm5J49VRCrL29HcHBwUhOTt60KrHXmpmZQVtbG+x2OzIzM5GQkMDk9Q6n1+vR3d2NyclJxMTEID093eurEdNi27m/mNVqRV1dHY4fP+6xmI1GI86fP4+CggLExcYCnY8DHf8DCBcQlAIoVnn+IMRcE337JBBdBuz9NhC4vvYNN160IKKthwkxIvJ7Qgj09vaiq6sLOTk5SEpK2jYnlOu10eqx/v5+DAwMoLS0dNOeq5GREVy7dg2lpaXbtmpvqxNC4MSJE5vWK24ldrvdPQ3SaDRCq9W6p0FuNDaz2Yyuri6Mj48jMTERGRkZmz7tyFMJMZPJhKamJvfqbJtZJfZaNpsNnZ2dGB0dRXJyMnbt2sUvgTuIzWZDb28vBgcHodVqkZGRwb5gW8SN/cVKSkoQFhbm65Bu6vz588jMzPTYIkXDw8O4du3awgV5hACGXwJavwOYegF1GKCJAhTLfC4JATj1gG0cUAYBKW8Fcj4KqNdf/drW1gatVuuTz2QiujkmxIjIr01MTKCpqQlxcXHYvXu331Yfmc1mDA0Nuatv5leuXKl67Pr165icnMSBAwc27QvR7OwsLl68iP379++YhsJbSU9PD6xWK3Jzczf92LIsY3JyEsPDw5iamoJKpUJ8fDzi4+Oh1a5tmspShBAYHh7G9evXoVAokJmZidjYWJ99mfdUQgwATpw44a4K81WV2I1kWUZ/fz+6u7uh0+mwe/duj7yGtPlcLhcGBwfR19cHAO6+YP76u3Kr2y79xf5/9v47OrJuPe8Df4UcC6EKqCrk3GikzgA6f31HukyyKYpeNqWRKZm2JXs0XpRJU2Foe1lpSeJIljTLY41lyRxzxrLlEe0r0iZpUbzdX0egcyM0GkAhAxVQOcdz9vyBr+oC3ehuhCqgAOzfWrU6oHDOrqpT55z97Od93kzmVAohmJmZwe/3c+3atd2PzegmLPwTsPzultiFgLwSyCsGNCCSoIRBqFBQDrWXoeNPQ93NQ48vGAzy/v17hoeHD70tiUSSeaQgJpFIziTbbxoHBgb2Hbx9mkm5x6xWKx6Ph9LSUhoaGnZ1j83NzREOh7lw4cKRTb5jsRjPnj2ju7tbrrhmkFS48b17946kFFYIQSAQwGq1srm5iaqq6HQ6jEZjRl0nkUiExcVFbDYbJpOJjo6OnPi+f/vtt9y5cycjr/Pt27e0tLRQW1sLHK9L7GNcLhdzc3MoikJ3d/exipCSvSGEwOl0srS0RDgcprGxkZaWlpwVVySf4nA4mJqaoqGhge7u7pzKFxNC8PDhQ65du3bo7ovJZJLnz5+j0+no6en5+rkl5gb7D8H3HryTELV/J4KVQVU/VJ0H/U2o6oMMnqe2L1pIJJLcQgpiEonkTHESywqOm3A4nM4eSyQS6c6VNTU1aDQapqenATLXMn0PKIrCy5cvqaqq4ty5c/ImMwPMzMxQVlaW0XDjj4nFYukcsHA4jFarxWg0Ul9fT2FhYcb2I4TAbrezsLAAQEdHB0ajMaeOk4cPH3Lr1q2MTFQdDgd2u52BgQGAnHCJfUwkEklnAba2ttLW1iZdRjlGIBBgaWkJp9OJXq+nra1NNko4wWzPFzt//nzOZPttbGzgdrsZHBw81HYCgQDPnz9ncHCQ+voDhuanpsFZfl/evn1La2vrqcumlUhOA1IQk0gkZwIhBEtLSywtLeXUjeFJQ1VVnE5nOnuspKQEk8mEw+GgsrKSc+fOHdlYUmUSwWCQq1ev5tQK+EkjFovx9OnTjAsoiqLgdDqxWq14vV6KiorSZZCHdQbsRjQaZXFxEavVisFgoLOzM2fz5h4/fsz169czIgqpqsrDhw/55ptv0v+XSy6x7SiKwsrKCsvLy+h0Orq7u7NyLEj2RiwWY3V1lY2NDcrKymhra6Ourk5eH08RubQQqKoq9+/f5+7du4fKF7RYLMzOzjIyMnIizh8fL1pIJJLcQQpiEonk1LO5ucnU1BRNTU10dXVJ4SSDRCIRLBYLVquV1dVVjEYjFy5coLa29sgmVGtraywuLnL9+vVjD4I/qbx+/ZqmpqaDr7J/hxACn8+H1WrF4XAAoNfrMZlM++5mup99OhwOzGYzqqrS3t5+IjqtPXnyhJGRkYyFzj979oyLFy+mBcBcdIltJ/W5zc/Pk5eXR3d3N3r9wbq4SfaHoihYLBZWVlYQQtDa2kpjY6N07J1yciFf7MOHDxQXF9Pe3n6g3xdCMD09TTgc5sqVKyfmmN1t0UIikeQGUhCTSCSnlmAwyLt37ygtLWVgYECKJVlGURR++MMfkp+fj0ajoaSkJJ09lu0bb7fbzZs3bxgeHqaysjKr+zptBINB3r59y61btw70+5FIJF0GGY1Gqa6uxmg0UldXl9UOg7FYjKWlJSwWC3q9ns7Ozh91FjsBPH36lGvXrmWsVHRlZYVkMklnZ2f6/3LVJfYxoVCIubk5vF4v7e3ttLS0yIWLDCOEwOVysbS0RDAYpKGhgdbW1pzI05McLceVL3ZYJ3IikWB8fByDwUBXV1dOCv1f4tmzZ1y6dEl+5ySSHEMKYhKJ5NQRj8eZnp4mFAoxNDQkM1COEFVVefr0KR0dHdTU1GC1WrFarSQSibRTKFvusXA4zNjYGP39/RgMhoxv/7Ty7Nkz+vr69lxGk0wm2dzcxGaz4fP5KC0tTZdBZvtGPxX2vbCwQDwep6Ojg4aGhhMpnoyNjXHp0qWMicWxWIwXL17sEDZz3SX2MclkkqWlJdbW1qivr6erq0tOHg9JMBhkeXmZzc1NdDod7e3t8poo+SRGoqGhIev7fPnyJa2trdTV1e37d/1+Py9evGBoaOhAv58LrKysoCgKHR0dxz0UiUSyDSmISSSSU4MQgoWFBVZXV+nr68NoNB73kM4kiqLw5MkTent70yV4qqricrmwWCy43e509pjJZMqoeyyZTDI+Po7RaNzhlJHsjsvlYmFh4Yvt4IUQeDwebDYbDoeDvLy8dGMFrVZ7JEJLPB5neXmZ9fV1amtr6erqoqKiIuv7zSbj4+NcuHAho4LPt99+y61bt3aUEZ0Ul9h2hBDYbDbMZjNFRUX09PTIMOp9EI/HWV1dZX19ndLSUtra2mR3T8muJJNJpqen8fl8Wc0X8/l8TE9Pc+PGjX3/7vr6OmazmZGRkZzNhNwL8Xic58+fH9iNLZFIsoMUxCQSyanAZrPx/v17Wlpa6OzslDf+x0wymeTx48cMDg6i0+k++XkkEtnhHtPpdDQ0NGTEPSaE4N27dwghuHjxojwWPoMQgocPHzI8PPzJJCMUCqXLIBOJBDU1NRiNRvR6/ZFltgghcLvdmM1motEo7e3tNDU1nUg32G6kuqNlcoL3/v17amtrdywGnDSX2Mf4/X7m5uYIhUJ0dHTQ2Nh4ao6BTKKqajoXTFVVWlpaaGxszGrZsuT0EAqFePfuHcXFxQwMDGQ85uDRo0dcvnx5X2XtQggmJyeJxWJcuXLlVHzvd1u0kEgkx4sUxCQSyYnG7/czMTFBRUUFfX19Micsh4jH4zx+/JjLly9TXV392edtd495PB6Ki4sz4h5bXFzEYrEwOjoqJ4W7YLFYcDqdDA0NkUgksNvt2Gw2AoEAZWVlmEwmDAbDkQcvJxIJVlZWWF1dpaamhs7OzlNZ4vXixQv6+/sz2iHN5/NhNpu5cuXKjv8/iS6xj4nH4ywuLrKxsUFDQwOdnZ1n/nyfEo2XlpYIBAKYTCba2tpkmankwGQjX8xiseBwOLhw4cKefycejzM+Pp7+rp8WZmZm0gtMEokkN5CCmEQiOZHEYjGmpqaIRqNcuHDhxJdPnVai0ShPnjzZV9h9yj1ms9mIx+PodDpMJhM6nW7fDpdUh9HR0dET0Zr9qEgmk/ze7/0ezc3NeL1e8vPzMRgMGI1GKioqjsVJ5PF4MJvNhMNhWltbaW5uPtWr6K9eveLcuXMZPXcJIbh//z737t3b8RmedJfYdoQQbGxssLCwQFlZGT09PVkr88pVQqEQy8vL2O12amtraW9vP3PvgSR7ZDJfTFVV7t+/z507d/bcQMTn8/Hy5UsuXry4q8P8JOP3+5mfn/9k0UIikRwfUhCTSCQnClVVmZ+fZ2Njg4GBgXRGlSR3CYfDPHv2jOvXr+9blEq5x6xWK263m+LiYoxGIyaTac8uiEAgwPPnz0/lzfVeEUIQDAax2WzY7XY2NzeprKxMvyfHVYqSTCZZXV1ldXWVyspKurq6zszE/vXr13R3d2e8K+qrV6/o7u7+xFU3NTVFTU3NiXaJfYzX62Vubo5oNEpXVxcmk+nEC36fIx6Ps7a2xvr6OsXFxbS1tWEwGE7t65UcP4lEgvfv3+P3+7lw4cKBnLrz8/Pk5eXt2eW1urrK4uIio6Ojp9LpKITgwYMHp2JxQiI5LUhBTCKRnAiEEFitVmZmZmhvb6e9vV3eTJwgUqLUzZs3D3WTG41G09lj8Xic2tpaGhoavuoei8fjjI2N0dbWRktLy4H3f5KIxWLpMshQKERFRUW6y+ezZ8+4d+/esQlhqdK+QCBAS0sLLS0tZ66s9c2bN1kpB7VarXi9Xs6fP7/j/+PxOE+ePDmVE7FYLMbCwgI2m42mpiba29v37EbJZVRVxWq1srKyQjKZpKWlhaampjP3XZEcLwfNF0vFJnzsWN2NVPanoihcunTpVOSFfY5Xr16dqcUfiSTXkYKYRCLJeXw+H+/evaO6upq+vj45GTih+Hw+Xr16xa1btzKS/SOE2NG5sqioKJ09tpvopqoqr169orS0lP7+/lMnCqiqitPpxGq14vF4KCwsxGAwYDKZdgQZT01NUV1dTVNT05GOT1EUVldXWVlZoby8nK6urjPdOfDt27e0tbV9MV/vICiKwuPHj7l79+4nPzuNLrHtqKrK+vo6i4uLaLVaenp6Tlw5faqr69LSEn6/H6PRSFtb24nuric5HaQiCJqamujq6vqqaPX69WsaGxsxGAxffF4sFmN8fJzm5mba29szOeScxGaz4fF4Plm0kEgkx4MUxCQSSc4SjUaZnJwkmUwyNDS0r+5EktzE5XIxOTnJrVu3Mi5sptxjNpuNWCy2q3tMCMHc3Bwej4dr166d6IwqIQR+vx+bzcbm5iaqqqLX6zEajZ/t1hmNRhkbG+Pu3btHJgj6/X7MZjM+n4/m5mba2tqkqA1MTEzQ3NycFVHw8ePHDA8PfyI8n2aX2Me4XC7m5+dJJpN0d3dTX1+f0685HA6zvLyMzWajpqaG9vb2jIulEslh2Z4v1tfXh8lk2vV5gUCAd+/ecevWrS9uz+Px8Pr1ay5dukRtbW02hpxzqKrKo0ePdl20kEgkR48UxCQSSc6hKApzc3PY7XYGBgbQ6/XHPSRJBtnc3OTDhw/cvHkza4JUyj1mtVpxuVyfuMcsFgtzc3MnLqckGo1is9mw2WxEIhGqqqowGo3U19fvSWR6+fIlbW1tWf9OpZw6S0tLlJaW0tnZeWbz2z7H5OQkjY2NWZkELiwsUFBQQGtr6yc/O+0usY+JRCLMz8/jdDppbW2lra0tZ4TwRCLB2toaa2trFBUV0dbWhtFozGnhTiKBr+eLPX78mAsXLnwxI3FlZYXl5WVGRkZO1HU4Ezx58oSrV68eeRdniUTyKVIQk0gkOUOqe9js7CydnZ20trbKicEpxWKxsLi4yI0bN44kKyQlJFmtVqLRKLW1tVRWVrK4uMi1a9dyNstDURQcDkc6F6qkpCTdDXK/DQr8fj9TU1PcuHEjS6OFYDCI2WzG7XbT3NxMa2trRspjTyNTU1MYjcasiJORSIS3b99y/fr1T352llxi20mV7C4vL1NbW0t3d/exdJ5VVRWbzcbKygqJRILm5maam5ula1JyIgkGg7x7946SkhIGBwcpKirCbrdjsVi4dOnSrr+jqirv3r0D4MKFC6c6L+xzLC4ukp+fv+uihUQiOVqkICaRSHICj8fDxMQEOp2O3t5eOTk4A6yurmKxWBgZGTnSibkQArfbjcViSd+4nz9/noGBgWPP6RFC4PV6sdlsOBwOAOrq6jAajVRXVx/qfXry5AlDQ0MZ72qoqiobGxssLS1RVFREV1fXV5scSOD9+/fU1dVRV1eXle3fv3//s6LXWXOJbUcIgdPpZG5uDo1GQ3d3N3q9PqvHa+p7vbS0hNfrTeeCHYcgJ5Fkg1S+WGNjI+vr69y+fXvXxZBYLJZucHOWxaBoNMqbN292XbSQSCRHixTEJBLJsRKJRJicnEQIwdDQ0LELEpKjZXFxEZfLxdWrV49NQAmHw9y/fx9FUaisrNyRPXYUK9fhcBibzYbdbicWi1FdXY3RaKSuri5jpV0Oh4PV1VWuXLmSke3BVuexhYUFnE4njY2NtLW1yfKPfTAzM4NOp6O+vj4r25+cnEwfRx9zVl1iHxMKhZifn8fj8aQn6Jn8zofDYVZWVrBarVRXV6dzwc7yey45vQghePLkCTabjZs3b36SL+Z2u3nz5g2XL18+0w1VUjx48IA7d+6cSYecRJJLSEFMIpEcC4qi8OHDB5xOJ4ODg2cmTFXyKbOzs0QiES5cuHBsE0UhBNPT00QiEdra2rDb7TidTgoLC9PZY5kSa5PJJHa7HZvNht/vp6ysDKPRiMFgyEqOihCCb7/9NiN5aUKIdLlrQUEBnZ2d1NXVyQn+Afjw4QM1NTVf7cB2UFwuF+vr61y4cGHXn59ll9jHJJNJlpeXWV1dpb6+nq6urgN/V5LJZDoXrLCwkNbWVoxGo5z0Sk49iUSChw8fcuvWLWZmZggEAul8saWlJdbW1hgZGZELJ98xNTVFfX191hZFJBLJ3pCCmEQiOVKEEKyurmI2m+np6aGpqUlOpiVMT08D0N/ff6zjSGUMjY6OUlRURCwWw2azYbFY0tljJpMJvV6/5wluqkTTZrPhdDrJy8ujvr4eo9GIVqvN+vG/traG3+8/1HsbiURYWFjAbrfT0NBAe3v7mQtBzjSzs7PppgjZQAjBgwcPuHfv3q4/ly6xTxFCYLfbMZvNFBQUcO7cuT05WVK/t7S0RDweT+eCFRYWHsGoJZLc4O3btxgMhrQzLBgM8vbtWxwOB83NzVy5ckWea7bh8XhYWVnh4sWLxz0UieRMI0N6JBLJkeFyuZicnKS+vp5vvvkmZzp9SY6fvr4+3r17x9zcHD09Pcc2jpaWFsrLy3n06BEjIyNUVFTQ2tpKa2trWtiyWq28f/+ewsJCjEYjDQ0Nn7jHgsFgugwykUhQW1uL0Wjk/PnzR+oUUVWVubm5A7V3F0Jgs9lYWFhAo9HQ2dlJf3+/nNBkiLy8PFRVzdr2NRoNZWVlhEIhysvLP/l5UVERdXV1WK1WGhoasjaOk4RGo8FoNGI0GgkEAszNzREIBOjo6KCpqemT724qF8zj8WAwGBgaGtr1vZZITjvBYJBAILBD3CkoKEBRFDo6OnC5XJjNZjo7O6Vb8juqq6t5+/btcQ9DIjnzSIeYRCLJOuFwmImJCfLz8xkcHJTOEsmuCCF4+fIlOp2Ojo6OYx1LKBRifHycgYGBz5YzpNxjVquVYDC44ya/oqICk8mEwWA41i6Lc3NzFBQU7Ov9jEajLC4uYrVaMRqNdHR0yGy/LGA2myktLc1qyeLa2hrRaJTu7u5dfy5dYl8nkUiwuLjI+vo6JpOJhoaGtGu0qqqKtrY2amtr5fsnOdM8ffqU/v7+dMdml8vF27dvuXr1KlVVVQghWFxcZGVlhb6+vqw5Y08az58/p6+vj4qKiuMeikRyZpGCmEQiyRrJZJKZmRk8Hg9DQ0NUV1cf95AkOY4QgrGxMZqammhubj7WsSQSCcbHx2lsbKS9vX3Hz1RVxeVyYbVacbvdFBQUUFZWhqqqhEIhCgoKMBqNmEymY+skl0gkePToEffu3fvqZF0IwebmJmazGSEEHR0dmEwmOcnPIgsLCxQXF9PU1JS1fSQSCcbGxrh9+/ZnnzM1NZVuJCHZnVQu2PT0NB6PB6PRyNWrV2UwuETCp01bFhYW0h2kP14QSiQSTE9P78gXO8tsbGwQDAY5d+7ccQ9FIjmzSEFMIpFkHCEEy8vLLC4u0tvbS0NDg5xYS/aMqqo8ffqUzs7OT7pUHTVCCN6+fYtGo6G9vR273Y7dbkdVVXQ6HUajkdra2k9KQOLxeNpFEolEqKmpoaGhYV/ZY4dlYmICvV7/RaEjFoul3WB1dXV0dnYem4B31lhaWiI/P5+Wlpas7ufhw4fcuHGDgoLdUzKkS2x3UiLx0tISsVgsLdIXFRXh8/mYnZ0lGo3S2dkpr3GSM4sQgvv373Pz5k0KCwt59eoVxcXFDA4OfvE7EQgEmJiYoLS0lIGBgWN1Uh8nyWSSp0+fcufOneMeikRyZpGCmEQiySgOh4OpqSlMJhM9PT0yK0JyIBRF4cmTJ/T29h5bB6ZYLIbdbsdqtbKxsUEikWB0dJSGhoZ9hWULIfB6vVgsFpxOJ/n5+enOldkSn8LhMC9fvuT27dufTEqEEDgcDhYWFkgmk7S3t9PQ0CC/q0fM8vIyGo2G1tbWrO5ndnaWioqKL5ZmSpfYj/D5fCwtLeF2u6mvr6etre2z5UyxWIyFhQWsVitNTU10dHTIIH3JmSLVSKKlpYXx8XG6urr25Xq12+1MT0/T3Nx8ZvPFHj16xOjoqDx3SCTHhBTEJBJJRgiFQrx7947i4mIGBgZkW23JoUkmkzx+/JihoSFqa2uzvj9FUXA6nVitVrxebzo0PyVcpW7cr1+/fqhMrZR7zGq1Eg6Hqa6upqGhgbq6uoxNBsbHx+np6dlR0hWPx1laWmJjYwOdTkdXV5cMAD9GVlZWEELQ1taW1f0EAgFmZmYYHh7+7HPOukssGo2ysrKCxWKhsrKStrY2dDrdnt8LVVVZX19ncXGRyspKenp6qKyszPKoJZLjJZlM8u233zIwMMDU1BTXrl07UAnkWc8XM5vNFBcXH3tMhERyVpGCmEQiORSJRIL379/j9/sZGhpKB6pKJJkgHo/z+PFjrly5kvFjSwiBz+fDZrOxubmJEIK6ujqMRiM1NTW7Tob9fj8vXrzg0qVLGRHpdnOPpTpXHtQ95vV6+fDhA6OjowghcLlcLCwsEIvFaG9vp7Gx8Uyuwucaq6urJJPJI2kg8cMf/vCrWXJnzSWmKArr6+usrq6Sl5dHa2trRpySbrebubk5EokE3d3dGAyGMykySk4/7969IxKJkEwmGRkZObTDKZUvFgwGuXDhwpkRlVONp0ZHR497KBLJmUQKYhKJ5EBsX9E7f/78sWc9SU4v0WiUJ0+eMDIycuhOTJFIBJvNhs1mIxqNUlVVhclkoq6u7rMZSx8Ti8UYGxujs7Mz44HomXCPPXr0iMHBQRwOB2tra9TW1tLZ2XlmJhcnhfX1dWKxGJ2dnVnf15s3b2hra/tiCPxZcImlyoWXlpaIRCI0NTXR0tKSlfyiaDTK/Pw8DoeDlpYW2tra9nyOkUhynUAgwG//9m8zNDREf39/Rs8ZZzFf7P79+6f63CuR5DJSEJNIJPtGZj5IjppwOMyzZ8+4fv36vpxTyWQSh8OBzWbD6/VSUlKC0WjEaDQequxRVVVevnxJRUUF58+fz8pNbMo9ZrVacTgcafeYyWT6bKnj3NwcU1NT6HQ62traaGpqIj8/P+NjkxyejY0NIpEIXV1dWd/X5uYmDoeD/v7+Lz7vtLrE/H4/S0tLuFwu6urqaGtrOzKBWFEUVldXWV5epqamhp6eHtm4QnKiCYfD/OZv/ibXrl2jt7c3a/s5S/eaExMTNDY2otPpjnsoEsmZQwpiEolkz6RW7crKyujv7z8Tq3aS3CEQCPD8+XNu3rxJSUnJrs8RQuDxeLDZbDgcDjQaDfX19RiNRqqqqjIqXAkh+PDhA36/n2vXrmX9Zj0ej2O327FYLGn3mMlkoqamhvX1dVZWVrDZbHz/+99Hr9dndSySw2OxWAiFQnR3d2d9X6qq8vDhQ7755psvPu80ucRisVg6F6y8vJy2tjb0ev2xvS4hBE6nk/n5eQC6u7uPdTwSyUHY3Nzk+fPnVFVVcfv27azvTwjBwsICq6urpzpfzOl0YrFYGBoaOu6hSCRnDimISSSSrxKPx5meniYUCp2pXAdJ7uH1enn9+jW3bt1KC7LhcBir1YrNZiORSKSFIr1efyTuqPX1dcxmM9evXz+yZhJCCNbW1nj79i1ut5va2lpqamqorKzk4sWLRzIGyeGw2Wz4fD7OnTt3JPt78uQJV65c+ayYnOIku8QURWFjY4PV1VWAdC5Yrrkkw+Ewc3NzeDwe2traaGlpybkxSiTbEUIwNzeH0+kkFotx48aNr55LMslpvw8VQvDgwQPu3bt33EORSM4cUhCTSCSfRVVVFhYWWFtbo7+/H4PBcNxDkkiw2+2Mj49jNBoJhUKUlZVhMpkwGAzH1t3U4/Hw+vXrA3fZ2iup8quVlRUqKiro6uqiurqaaDTK7/7u72I0GtPZaKnsMTnRzk3sdjsejyerJUfbWVpaAqC9vf2LzztpLrGU82ppaYlwOExjYyMtLS0notNxMplkeXmZ1dVV6urq6OrqOlQpt0SSDRRF4cWLF2i1WsrKyohEIpw/f/5YxhIIBHj37h3l5eWnrlJhbGyMoaEhWVItkRwxUhCTSCS7YrVamZmZobW1lY6OjhMxMZKcTlRVxe12Y7PZ0p0Yi4uL8fl83Lt3L2eCqiORCGNjY5w/fz7jZR1+vx+z2YzP56OlpYXW1tYdr/vDhw+UlpbS2tqa7p5ptVrZ3NwkLy8v3bnyc9ljkqNnc3MTl8t1ZBPLWCzGy5cvuXnz5lefexJcYoFAgKWlJZxOJ3q9nra2tqyK0dlECIHdbsdsNlNQUEBPT09GuthKJIclFAoxPj7O+fPnqa+v58GDB3zzzTfHvtCSyhdraWmhs7PzVNyjrq2tEYvFjiRXUiKR/AgpiEkkkh34fD4mJibQarX09fUduo22RHIQAoEANpsNu91OMpmktrYWk8mETqdLZ3VZLBaWlpa4fv16zoTtKorC+Pg4dXV1h86GUhSF9fV1lpeXKS0tpaura9dJ8tccPYlEIp09FgqFpHssR3A4HDgcDvr6+o5snw8ePODOnTtf/b7kqkssFouxurrKxsYGZWVltLW1UVdXl1NjPCyBQID5+Xn8fj8dHR00NTVl7/zm88HcHJjNsLkJigKlpdDaCp2d0NUF8hxxZkmJTsPDw1RUVDA9PY1Wq6W5ufm4hwbszBc7DVUMiUSCsbGxI8lmk0gkP0IKYhKJBNiaaExNTRGLxbhw4YJ0kkiOlFRgvNVqJRQKUVFRgdFoxGAwfLEkYnV1FYvFwsjISM5MioUQTE5OkkwmuXTp0r7HFQgEMJvNeDwempubaWtr+6Iw/ebNGxoaGvY0GRBC4Pf7sVgs6aYDqc6VFRUV+xqn5HC4XC6sVisDAwNHts/p6Wn0ev2ejpVccYkpioLFYmFlZQUhBK2trTQ2Np56MTeRSLC4uMj6+jomk4nOzs7MlYFOTcFv/Rb87u+C1wuJBHx8niot3RLFfvZn4Sd+AqqqMrNvSc4jhGB2dhaPx8O1a9coKCggEonw/Plz7ty5kzPX2hSnKV/s4cOH3Lx589Sf3ySSXEIKYhLJGUdVVebm5tITs7q6uuMekuQMoKoqTqcTm82G2+2msLAQg8GA0WjctzCzsLCAx+PhypUrOXWjvry8zNraGqOjo191WqqqmnaDFRcXp91gX3s9oVCI169fH3hFebt7LBgMpt1j9fX18oY8y7jdbjY2NhgcHDyyfXq9XhYXF7l8+fJXn3ucLjEhBC6Xi6WlJYLBIA0NDbS2th5piHeuIITAYrGwsLBASUkJPT09VFdXH2xjgQD8N/8N/LN/Bn7/lshVXQ1FRTsFMVWFcBiczi2xrKsL/pP/BO7e/VQ4k5wqkskkL168oKamhnPnzqW/++Pj43R3d+d0Ke9pyBebnZ2lsrLy2BciJJKzhBTEJJIzSuom+8OHD3R0dNDW1pZTYoLkdJFyJtlsNjY3N1FVFb1ej9Fo3JPw8zVmZ2eJRqNcuHAhQyPODA6Hg8nJSUZGRnZ1XYZCIcxmMy6Xi8bGRtrb2/d1E//s2TP6+vqoyoB7Yzf3mMFgwGQynegV91zF4/GwtrbG0NDQke1TCMH9+/f53ve+t6fnH7VLLBgMsry8zObmJjqdjvb29hObC5YNfD4fc3NzRCIROjs7aWho2Pu5c3kZfuVXYHISampAp9ubuJVIwNralmj2J/8k/OIvQo7kNkoySzAY5Pnz55+UH7rdbubn5xkZGTnG0e0dm83G+/fvT2S+WCAQ4MOHD1y7du24hyKRnBmkICaRnEG8Xi8TExPU1NRw/vz5nAkll5wuotFougwyEomg1WrTZZDZOOampqbQaDT09/dnfNuHIRQKMTY2xoULF9Dr9aiqms4/KygooKurC71ev++bdrfbjdlsZnh4OCvjTiaT2Gw2rFYrgUCAqqqqdDdP6R47PD6fj6WlJS5evHik+33x4gW9vb17EjmPwiUWj8dZXV1lfX2d0tJS2traqK+vP1GT2KMmFouxuLiIxWKhsbGRzs7OL7tQV1bgP/qPYHYW2tq2xK394nJtlVf+yT8Jf+kvQY7kNkoyQ6qR0seLN0IIHj58yPDw8InqgKqqKouLiycyX+z+/fs5l98okZxmpCAmkZwhotEok5OTKIoiWztLMo6iKDgcDmw2G16vl+Li4nQZ5FEca0II3r59S3l5OT09PVnf335IJBI8evQo/e+Ghgba29sPnAl01JOUlHss1bkSwGAw0NDQIN1jB8Tv97OwsMClS5eOdL8WiwW/309vb++enp8Nl1hKFF5ZWUFVVVpaWmhsbJSLM/tEVVU2NjZYXFykoqKCnp6eT7+P0Sj8mT8D4+PQ0XE4d5fHsxXE/5/9Z/Bv/puHG7wkJxBCMDMzg9/v59q1a58sdqytreH3+3NuoWmvpPLFwuEwQ0NDJ+J69ebNG9rb2w9eGi2RSPaFFMQkkjOAoijMzs6yubnJ4OAgOp3uuIckOQUIIfB6vdhsNhwOBwB1dXUYjUaqq6uPZXVTCMHLly/R6/W0t7cf+f53G4/VamVxcRGNRkM8Hken0zE4OHio98diseB0Oo+03G47yWQynT0m3WMHI9VNcC95XplEURSePHnCnTt39vT8TLnEhBC43W6WlpYIBAKYTCba2trOZC5YNvB4PMzOzpJIJOju7sZgMGx9Xv/oH8Hf+3vQ1ASZeK/X17eyx/77/36rG6XkxJJMJnd0Rf74+60oCg8ePOCbb7458ef17fliAwMDOd1BfXNzE4fDcWJFSInkpCEFMYnkFCOEYG1tjfn5ebq7u2lubpYWbMmhiEQiWK1W7HY7sVgsLYTU1dXlzA2zEIKxsTGampqOrT18JBJhcXERm82GyWSio6MjPfGfn5/H4XAwMjJyoPcslQN1+/btnLipF0IQCASwWCzSPbYPQqEQHz584MqVK0e+70ePHu2p2UOKw7jEQqEQy8vL2O12amtraW9vz0jmnWR3otEoZrOZzc1N2oqLafuVXyEvkQCjMTM7UFUwm+Hf+rfgr/7VzGxTcuQEAgGeP3/O4OAg9fX1uz5nZmaGsrIyWk+R8HkS8sVUVeXhw4d88803xz0UieRMIAUxieSU4na7mZycRK/X09vbmzNiheRkkUwm2dzcxGaz4fP5KC0txWg0YjQac9rZoaoqT58+pbOzE5PJdCT7FEJgt9tZWFgAoKOjA6PRuOsNt81mY2ZmhtHR0X2XPC4uLqIoCt3d3RkZd6b52D2m1WrTnStlSdyPCIfDTE9PH0t4stlspri4eM+C8X5dYvF4nLW1NdbX1ykuLqatre1HjiXJkaCqKu6/83co/a/+K5ItLZRrtRRk6j7A4djKEPtf/1c4QdlMki02NjaYm5tjZGTks3EG0WiUsbEx7t69e+q+t6qqsrCwwNraWs7miz19+pTLly/n9H2WRHJakIKYRHLKiEQiTExMoNFoGBwcPFEhqJLjJ1XWZLPZcDqd5OXlUV9fj9FoRKvVnqgbY0VRePz4MX19fdTV1WVtP9FolMXFRaxWKwaDgc7Ozj1973w+Hy9fvuTKlSt7zgpJJpN8++233Lt3j7wTEGr9OfdYqnPlSTqeMk0kEmFycjJrTRG+RDgcZmJigtHR0T3/ztdcYqqqYrPZWF5eJplM0tLSQlNTkxRBjwsh4Gd+BrG0RMJoJBgKgRCUl5dTVFzMob55igJLS/Cf/+fwx/94pkYsyTJCiHSe1pUrV764UPrixQva29vR6/VHOMKjJR6PMzU1RSQS4cKFC1RUVBz3kNIsLy8jhMiJ6AeJ5LQj71IkklNCMpnkw4cPuFwuhoaGqKmpOe4hSU4IoVAoXQaZSCSoqanBZDJx/vz5EyG6fI78/Hxu3rzJ48ePGRoaora2NmPbFkLgcDgwm82oqkp7ezvnz5/fl8BTVVXFrVu3ePbsGd3d3TQ2Nn71d2ZnZ+np6Tkxn4tGo0Gr1aLVaunt7U07Dufm5ggEAlRWVqazx86acKLRaDiuNcmysjIikQhCiD0fsz09PTx58gSTyZT+HSEEHo+HpaUlfD4fJpOJS5cuyYWYXMBuB4sFjVZLUVERtUVFKIpCMBQiEAhQWlZGWWnpwUTp/PwtwW1mJvPjlmSFRCLB+Pg4RqOR/v7+L37uXq+XZDJ5qsUwgKKiIi5fvozf7+ft27dUVFTQ39+fE1EEJpOJly9fSkFMIjkCztbdp0RyChFCsLKywsLCAufOnfvqjY5EEo/H2dzcxGq1EgwGKS8vx2g0cvXq1QN3PcxVCgoKuHHjBo8fP+bKlSuHzi6KxWIsLS1hsVjQ6/VcvHjxUB00i4uLuX37Ni9fviQQCHDu3LnPfn+j0SgOh4O+vr4D7++4KSgooKGhIe0ySrnHUmWm9fX16eyx034e02g0qKp6bPuvra3F7XbvuclKUVERdXV1WK1WqqurWV5exmazUVNTQ2dnp+yIlmssLEAoBNsWAvLz86nSahFCEA6HcblcFBUVUV5evv9YhdJSmJjI8KAl2cDv9/PixQsuXLjwVZFLCMHExARXr149otEdP1qtllu3bmG1Wnn48CFtbW10dHQc6zWouLiYZDKJqqonZgFMIjmpyJJJieQE43Q6mZycxGg00tPTI3PCJLuiqioulwubzYbL5aKgoACDwYDRaKSiouLUCw+wJSY9efKEkZGRfZdFCCFwOp0sLCwQj8fp6OigoaEhozepQghmZmYIBoNcvXp1122/evWKlpaWrJZ/Hicp95jVasXv91NRUUFDQ8OpdY8lEglevHjBjRs3jmX/TqcTi8Wy506liUSCxcVFnj17Rk9PD+3t7Z/NyJPkAP/7/w6//MvwhaxBwZbIHwwGSSaTlJaWUlZWRn5+Pnlf+1w3N6GoCB4+BHkM5Czr6+uYzWZGRkb25Nzc2NhIVxqcRbbniw0MDHy24cBR8P79e3Q6XU5mnEkkp4nTd4cpkZwBQqEQExMTFBQUcP36dRm6KdmBEIJgMIjVamVzczNd+mAymejv7z+Tq40lJSVcv36dZ8+ecf369T25uuLxOMvLy6yvr6PT6RgYGMhaxohGo6Gvr4+1tTUePXrE9evXKSoqSv88EAgQjUZPrRgGn3ePLS4uIoSgvr4ek8l04rLsPsdxlkwC6HQ6Jicnv/icVC7YysoKiUSC5uZmLl68mP4sJDmMEAi2PkNFUbYeySSJRIJEIkE8kUD5zoECgEZDMBCgqKiI0rKyHYKYJi+P/Ly8LaEsP5/8vDwKFAWNEJy9q8nJQAjB5OQksViMO3fu7Om6r6oqHz584O7du0cwwtwkLy+P7u5uWltbmZqaYn5+/tjyxRobG1lYWJCCmESSZaQgJpGcIJLJJO/fv8fr9XLhwgXZul6SJhaLYbfbsVqthMNhKisrMRqNjIyM5EQeRi5QVlbG8PAwz54949atW7uWh6aaCpjNZqLRKO3t7XzzzTdHJiI2NzdTXl7Oo0ePGB4eprKyEoCJiQkGBwePZAy5QmVlJefOnePcuXMoioLdbsdsNqfdYyaTCaPReGLdY8ddMqnRaCgtLSUcDn8iEKdywbxeL0ajkQsXLqSfk+o4uT1LTHL0qKpKNBolHA6nH5FIhHA4TCKRoPzDB3rDYcJWK8p3x5oQAjQa8vPzKS0tpbSkhOKSEvLz8giFQoTDYfLy84lGo5SWlKDVaskvKECoKoqqon4nrMUTCZRQiGhhIZMPH6bHVFhYuLXdXR4n9Xt6EonH44yPj9PQ0LAvp9f8/DwdHR3ys2JnvtibN2+orKw88nwxrVaLz+c7sv1JJGcVecaTSE4AQgiWlpZYWlri/PnzDA4OyonIGUdRFJxOJzabDY/HQ2FhIUajkYGBAcrLy497eDlLZWUlV65c4cmTJ9y6dSvtwkokEqysrLC2tkZ1dTXnz59Hq9Ueyxhra2u5fv06Y2Nj9Pf3k5+fT3Fx8bGNJxfIz8//xD1mtVp59uwZqqqms8dOknssLy/vWB1iAA0NDVgsFrq6uohEIiwvL6czwtrb26murv7k/dyeJfa5jpOSw6EoSlrcSv2ZeqREVI1GQ0lJCfn5+SiKQjKZJB6Po6rq1p91dSSLiiiIxymoqqK4uJjioiIKCgt37TAZj8eprKwkGouh1+nw+Xx4vV4KCgqorKyksKAAPhJKyu7cSbuJhBAkk0kikUj64XK50n9PJpPp35PCWfZIdS++ePHinvMBYWtRbWNjg3v37mVxdCcPrVbL7du3jyVfTKPRUFlZid/vP9PXf4kk28gMMYkkx9nc3GRqaoqmpia6urrOZLmbZGuy4ff702WQQoh0GWRNTc2JEQFyBZfLxeTkJP39/SwvLxMOh2ltbaW5uTlnsviSySRjY2Nsbm7ykz/5k7I0+jMoisLm5iYWiwW/3095eTkNDQ056R4LheD9ezCbYW1NMD+/TE9POw0N0NUFfX3wnSnwSAiHw/yrf/WvqK6uprCwkNbWVoxG41evMymX2DfffCPPPfskmUzuKnSlun7CllhaVlaWzvRKZT/F43ECgQB+v59YLAaQdromEgni8TgFBQVUV1ejr63F+Au/QL7LBXvoYOt0udDrdDidTnQ6Haqq4vF4KC4uTm+3orKS/Ly8rQ6TZjP8xb8I/86/s6/Xv5twtv0hhbODs7q6ytLSEiMjI/u+Xpz2jMpMoKoqZrOZ9fX1I8sXs1qt+Hw+ent7s74vieSsIgUxiSRHCQaDvHv3jtLSUgYGBnbkCUnOBtFoFJvNhs1mIxKJUFVVhdFopL6+Xk4GDkEymWR1dZX3798TDAb5/ve/T+22Tmy5xNraGpOTk5hMJi5evCjFhz0QDAaxWCzY7faccY8tL8O/+BfwW7+1lUWemvOHQlvlihrNlvlGr4c/8kfgp396SyDLBkII7HY7y8vLxGIxPB4PP/7jP77vCfTU1BS1tbXSJfYdQggSicQOoSv192g0mha78vPzKSsrSz+2i14ajYZwOIzP50s/UsJXWVkZVVVV5OXlEY/H8fv9RKNRioqK0Ov16PV6qqurd4qZ/+AfwH/9X28F63/h2FdUFZ/PR21NDcFgkPyCAkpLShBC4PP50OTlUVxcTDAYpKiwkIpEgrxkEv6n/wk6OrL2fkrhbG8IIXj37h2KonDp0qV9L5z6fD6mp6ePrcHHSSMejzM5OUk0Gs16vpiiKDx+/PhM57pJJNlGCmISSY6RSCSYnp4mGAwyNDQkbdJniJTTxWaz4fV6KSkpwWg0YjQa99QdSvJlfD4fZrOZQCBAS0sLLS0tbG5usrS0xPXr13POfamqKvfv3+fu3busrq5isVgYHR09ExO8TJH6TqVW2VPuMYPBcCRZMIkE/I//I/yjfwQOB1RUQG0tFBdv6RMulwudTocQEI+D2w2BwNZzfuEX4Od/fuu5mcDr9bK0tITH48FgMNDW1kZ5eTkfPnxAq9XuW9iKx+M8ffqUu3fvnnqhVghBPB7/ROgKh8NpwQq2hJntIlfqUVJSsuM9EkJ8InxFo1E0Gk1a+KqqqqK0tJRgMIjT6cTj8QBbJVx1dXXodLqvXxfMZvgTf2JLbf1C+VwkGkVRFCrKy7fEMa93xyJB6jVXV1eTiMdRZmeJ3rtH9a//+rE7as+6cBaLxRgfH6e5uZn29vYDbePRo0dcunTpWILjTzJ+v593796h1Wrp6+vL2jXl8ePHDA8Py4VxiSRLSEFMIskRhBAsLCywurpKX18fRqPxuIckyTJCCLxeL1arFYfDgUajoa6uDpPJRFVV1amfZB4FiqKwurrKysoK5eXldHV1UVNTs+M5q6urWK1WhoeHc+o9n5+fJy8vj87OTuBH5dOjo6N76pIp+ZRU91WbzZZ2j2Xr++bzwV/+y/DgAZSWgtEIH2uuKUFsO0KA3Q7BINy4AX/7b285xw5CJBJhZWUFq9WKVqulra2N2traHa/V7/czNzfH1atX97390+ASE0Kkw+k/LmNMJBLp5xUXF38idJWVlVFUVPTZY0cIQSQSwev1flH4qvou48vv9+N0OnE6nUQiEQoLC9Pur5qamoOJ9n/jb8Bv/Aa0t8NnJuw+v5/S0lKKvvu5y+2murp6qzzyOxKJBF6fj+pYjIKCAmx/9a/yvrCQlpYWOjs7c25B4WNSDr5oNHpqhDOPx8Pr16+5dOnSgV3OVqsVu93OxYsXMzu4M4TVauX9+/dZyxdbWFigoKCA1tbWjG5XIpFsIQUxiSQHsNlsvH//Pn1jmUuTcklmCYfD6TLIeDxOdXU1JpMJvV5/7Cvtpwm/34/ZbMbn89Hc3ExbW9sXJzILCwt4vV4uX76cE9+/RCLBo0ePuHfv3o7xBAIBnj9/vu/AZMmnKIqCw+HAYrGk3WOpzpWHXekPBOCXfgkePtyKb/pcn4vdBLEUkQisrsLIyFbl217nu8lkko2NDVZWVsjPz6etrQ2TyfRFweKHP/zhJ8faXsh1l9hunRhTD0VR0s8rKSn5ROgqLS2lsLBwT68rJXxtd3xFIpF0J8+qqiqqq6vTwpdGoyGRSOByuXA6nbjdblRVRavVpgWwjIneXu+W1XB+fqu8cZfjwOVy7RBKI5EIiqpS8dGBqwYCRJeXCf6pP0Xdf/FfIIRgeXmZpaUlOjo6aGtry8njYD+cFOFsZWWF5eVlRkdHd+2YvBdSLuQ7d+7IbtSHJJv5YpFIhLdv33L9+vWMbVMikfwIKYhJJMdIym5dWVlJX1+ftEOfQhKJRLoM0u/3U1ZWhtFoxGAwyJD0DKOqKmtraywvL1NaWkpnZ+e+RKMPHz4Qi8W4cOFCFke5NyYmJtDr9bs6b+LxOGNjY7S1tdHS0nIMozudhEKhdPaYoijU1dXR0NCwb/eYEPDX/hr8D/8DNDdvucM+x5cEMYBYDFZWtjLF/vbf/nwMlBAiXf4bi8Voamqiubl5z9eU169f09nZSVVV1Z6ev53jcolt78T4cSnj9k6MH7u6Uv8+iGCxF+Er9dheJimEIBAIpN1f4XCYgoKCHe6vrC6IvH0Lf/7PbwXYtbfvEMWEELjd7h3HoRACl8uFfrs1MRgEiwXxYz/GzJ/+0/jjca5evUpBQQGKomA2m9nY2KC7u5umpqYTL4x9jY+Fs1RWXOrv2wXXTApnqqry7t07AC5cuHAoZ97HLmTJ4YnFYkxNTRGNRrl48WLGun4/ePAgZxceJJKTjhTEJJJjIB6PMzU1RSQSyXogp+RoSU0kbDYbLpeLvLy8dFlWZWWlvJnJAsFgELPZjNvtprm5mdbW1gOLy1NTU+Tl5dHX15fhUe6dSCTC8+fPuXPnzmePF1VVefXqFaWlpfT398vjKsOk3GNWqxWv10tZWVk6e+xrx9b9+1vusPJy+Kg69xO+JogB+P3g8cDf/JvwUz+182c+n4+lpSXcbjf19fW0tbUd6Hpis9lwu90HOu6z4RJLdWL8uIxxt06MH2d2lZaWHlpcSpVR+ny+dLljNBoF+KLwtX38291fiqJQWVm5w/115N/ZFy+2anjX1qChYSvQjq3PLxqNfpJX6vV6KS8vpzA/H6xWiEbh+9+Hv/7XoaIi7Wy/du0ald+1Rk0mk8zOzrK5ucn58+cxGAxn/ty0vdlCyq14UOEsGo0yPj5OW1vbocvn4vE4jx8/PpAzVPJ1fD4fExMTGcsXm5qawmAwyC6gEkkWkIKYRHKEqKrK/Pw8FouF/v7+I2nZLMk+wWAwXQaZTCapra3FZDKh0+lyPlflpKKqKhsbGywtLVFUVERXVxc6ne7QN/ZCCN6+fUtFRQXd3d0ZGu3+eP78OV1dXV/NhBFCMDc3h8fj4dq1a7LkNouEQqF09ljKPWYymaiurt5xzCkK/PE/DtPTe2u+txdBDLa6VLa2wm/+JggRZWVlBYvFQkVFBe3t7Yc+9lVV5eHDh3zzzTcH+v3JyUl0Ot1XXWK7dWJMPVKCE3zaiXF7OH0mz6nbha/tji/Ym/CV2kYq+N7pdBIKhcjPz0en06HX69HpdLnz3VxdhV/7Nfj2262Dta6OoEZDQWHhJ47leDhMwmajPJmEujr4s38Wfu7nduSQhcNhxsfH066w9O/G48zMzOD1eunr65OT+D2wm3C2/e+p/MNU5mFJSUlaCE79fT+Oszdv3qRFfkn2sFgszMzM0N7eTnt7+4HP0263m9XVVZn1JpFkASmISSRHgBACq9XKhw8faGtrO9RFUXL8xONx7HY7NpuNYDBIRUVFugxSlr1ml1AoxMLCAk6nk8bGRtrb2zP+ngshePHiBXV1dQfu2nVQfD4f79+/31dWiMViYW5ujtHRUVmGewSoqprOHku5x1LZYy9fFvEf/AdbIfh7qZTZqyAWiQhWVxP8h//hLMPDflpbW2loaMioOPTkyROuXr16oDyieDzOkydPuH79+q5ljLt1YvzY3fU5wSkTfEn4KikpSWd8abVaSktLvziOZDKJ2+3G6XTicrm2ujNWVKTdX+Xl5bl9fVcU+P3fh3/+z+HNG8JO55bQWFCwVUr5XUaW0GjwFhZS9Sf+BHn/xr8BnymrUxSFN2/eUFhYyNDQ0I7XHo1GmZ6eJhwOMzAw8ElDE8neWFpaYm1tjZGREYqKitLC2fZHSjjb7jgrKiqitLQ0nZGXEs6SySTT09PcunXrGF/V2SG1GL6xsXHgfDEhBA8ePODm8E3Wx9ZxzblwfXARcUdAA+V15eh6dejP6WkcbqSgJHeaP0gkuY4UxCSSLOPz+Xj37h3V1dX09fXlVIciyd5QVRWn05kuKyosLMRgMGA0GmW56xEghMBisbC4uEhBQQGdnZ3U1dVlddIphGBsbCydxXRUPH78mEuXLu07d8Tr9fLq1SuuXr16oBwoycHZ7h77J/+kkQcPGjh3TvNdicyXj9EvC2KCWCz+XSaWgt2u5ad/Oo//8r/MzjVkcXGRvLw82traPh3Jtk6MH5cypjox2u12qqqqMJlMn5QxfqkTYybZi/CVenxN+EptLxwOp91fgUCA/Px8amtr0+6vE3tNFwJmZ5n6n/9nBoqKYGNjSwyrqICuLujq4kNZGdrvxNevsV20+VhUDYVCTE1NoSgKAwMDn5RnSnZHVVXevHlDQUHBJ2Lj19juOPv4MTMzs2MB72PhLPX3XOuqedJJ5Yulskr3c50P2oP8zq/9DqHXIcL2MEIINBoN+YVb7lMloSCEIC8/j6qWKnr/aC/9/2Y/JdVykUwi+RpSEJNIskQ0GmVycpJkMsnQ0FDGgjUl2ScVgGy1Wtnc3ERVVXQ6HUajMSNleZK9EQ6HWVxcxG6309DQQEdHx4G7aR0EVVV5+vQpXV1dGI3GrO/PbrdjsVi4dOnSgX4/Go0yNjZGT0/PkQecS7b4Y39MMDeXpLZ2SyjKz8+npKSE4uLiXd1cuwliyWSCcDhMPJ6gqKgoHQBvtW51mvw//o/Ph+sfBFVViUQieDweJiYm6Ojo+KQTo0aj2TFZ3q0TY8ol9s0332T9HCmEIBaLpTO+/H4/4XAYOJjwlUJRlB3ur2QySVlZGXq9nrq6OioqKk7V+T8cDjM1NcXw8PBnf/727Vtu3Lixp+2lhPnPdcH1+/1MTU2Rn5/PwMCAvC/6AqnzeWdnZ0YXZex2OxsbG1y+fBn4snAWiUR2dZxtf0jhbP9szxfr7+//4nsnhMD8u2bG/v4YzgUn+SX51DbVkl+0exl2MpYk7AiTiCTQ9ei48Z/coOV2y6k6b0kkmUYKYhJJhlFVldnZWex2OwMDAzu7NElylmg0mi6DDIfDaLVajEYj9fX1sh35ESKEwGazsbCwgEajobOz81iDmRVF4fHjx1nPwUmVQ9y4ceNQop+iKDx//pza2lrOnTuXwRFKvobHAz/xE5CfvyVcwdbnEY1GicViCKFSVFRMSUlx2j2WEsRUVUmXPaXys7bcGz867v1+CIfhBz+Axsa9jUlRlF2D6T/XiTFVelteXn6gTox7zRLbDx87vlLCV3FxcVr0qq6u3pfwlWK7+8vv95OXl7fD/XXaz/0rKysoikLHFwLvHj58yOjo6J5L0+PxOM+fP8dgMNDV1bXrZ+LxeJiamqKsrIz+/n5Z6v0RLpeLt2/fZtzxK4Tg/v373Lp1a99RA4cVzlKPnMnTywG+li+mKipjf3+Md7/xDgRUNFbg8/n2VGavJBR8az4KSgq49n+5xqVfuCRFMYnkM0hBTCLJEEIINjY2mJ2dpbOzk9bWVnnxyWFSXeRsNhsej4fi4mKMRiNGo5GysrLjHt6ZIxqNsri4iNVqxWg00tHRQWlp6XEPC4BEIsHjx4+5ePFi1jJwVlZWCIfDnD9//tDbEkIwPT1NNBrl8uXLsrHDEbG8DD/7s6DVphv47UAIQTweJxaLEo9vucei0WhaAE25LT533YhEwOmEf/pPoa9vZyfG7ULX5zoxbnd3fW5iOjU1RX19/YEbvhzGJbYX4auqqurAHRpVVcXj8eBwOHC5XCQSCUpLS9PZX1qt9sxds1+9ekVPT0+6S+RurKyskEgk6Orq2vN2hRC8f/+eYDDIlStXPiusOhwO3r9/T3V1NefPn5cZnMDCwgIWiyWdF5bpbauqmtWGMVI42x/b88UGBwfTC29CCMb+3hiv/8lrSqpKKNNt3Ze6XC5qamvI0+ztuh60B0lGk1z/5etc/FMXs/UyJJITjRTEJJIMkCo10el09Pb2Sut4DiKEwOfzYbVacTgcAOj1+l07xUmOBiEEm5ubmM1mhBB0dHRgMply8rOIxWI8efKEK1euZDyjS1EUHjx4wDfffJPRScDq6irLy8v7cndIDs7XBLGPURSFRCJOUVEReXnbP3eBqgoURdnxCIdVPJ4CfvVXZ+joCFNQUPBJCeNhOzF6PB5WVlYO1cnsay6xVKljqtwx08JXikgkknZ/+Xw+8vLyqK6uTgtg8jsBDx484O7du198nxVF4dGjRwfqQGqz2Xj//j3Xrl37rOgmhMButzMzM0N9fT3nzp07k/dQqqry6tUriouLGRwczPh1MJFI8PDhQ+7du5cTiyRSONvJx/litqc2fv9Xfp/CssK0GAYQDAXJz8+ntGTvC4ZBWxCAn/p//RQNV2ScgkTyMVIQk0gOQSQSYWJiAoChoaGccbRItohEIthsNmw2G9FolOrq6nQZ5Gm5iTqJxGIxFhcXsVgs1NfX09nZeSJcedFolCdPnjAyMpLRZgofPnygpKRk1zDzw5Iqvcn0mCWf4nLBT/4kFBbC542EAlVVPxG7FEVBVX90O5aXpyE/P3/HIxzOJxzO43/5XzS0tGTnNaRKqr73ve8deBvbO076/f60+BUKhYCtye32UsfDCl+wJSZ4vd60ABaPxykpKUmLX1VVVTkptB8nyWSSZ8+ecfv27a8+9/nz55w/f/6LTrLPEQqFeP78OT09PTR+odZXCMH6+jrz8/M0NjbS1dV1Zq7TkUiE8fFxurq6aGpqyso+3r17R319PSaTKSvbzxZnTTjz+Xy8+PYF5l8zo3gUajtqd/w8qSQJ+AP7cqsLIfAseKgfrOenf/2nKSqXiwESyXakICaRHABFUfjw4QNOp5PBwUFqa2u//kuSrJNMJtnc3MRms+Hz+SgtLU2XQcqMkuNFCIHD4WBhYYFkMpl2g+XCSvV+CIVCjI2NcePGjYwI4PF4nMePH3Pv3r2sTdhDoRDj4+MHbvcu2RuqKvjX/3WV1VVBfX3iO5FrN7Er7xOxKz8/77vvwuePAZsNqqq2QvWzObd7/vw5fX19exZQtzu+UsJXquNkc3NzWvwqLy/P2DEejUZ3uL+AHe6vo2y+cVLZ3NzE6XTS19f31ec6HA6sVitDQ0MH2peiKLx+/ZqioqKvdksUQrC8vMzi4iLt7e20tbWduOvEfnA4HExMTHDt2rWsdd8MhUK8fv16T+LnSeU0CWev/ttXPPrbj9DUaqiorKC0rBTNtmuD0+lEp9ft+L+vocS3MsW+9ze+x/mfOXw0g0RympCCmESyD4QQrK6uYjab6enpoampSa46HyNCCDweD1arFafTSV5eHnV1dZhMpjOZB5OLxONxlpaW2NjYQKfT0dXVdeI7iwUCAV68eMHNmzcPPfF++/YtJpMJg8GQodHtTjKZZGxsLN2tU7I/Up0YPw6o/7gT42/8Rid/8Ac62tuVHYJXJib0CwvwYz8Gf+/vHXpTX2R9fZ1wOExPT8+O/4/H4zuEr2Bwqwxnu+MrJXwlEgmePn361XK8vSCE2OH+isViFBcX73B/nWbBJFtMT09TX1+/p2YhKefgYYX7paUl1tbWGBkZ+eq5U1VVFhYWWF1dpbu7m+bm5lN1TRdCYDab2dzcZHh4OKsNHJ4+fUp/f3/Gy/1PIrkunCWjSf7Zz/4zAhsBtE1aQqEQ0WiUSm0lxUVb3xmff2vBt6hwf04vz6IH4yUjP/P/+ZlT9V2SSA7L2SvSl0gOiMvlYnJykvr6+oxn/Uj2TigUSpdBJhIJampqMBqN9Pb2ys8kRxBC4HK5WFhYIBaL0d7ezjfffHNqJq2VlZVcvnyZp0+fcuvWrQNPZEKhEIFA4FB5TXuloKCAmzdv8vbtW969e/dVl8ZZYrdOjKl/pzox5uXlUVJSks7pqqmpobGxkdLS0h15R0LA+Djk5UEmTanx+Naff/gPZ26bn6O2tpbJyUny8vJ2Fb60Wi0Gg4GKiorPHkNFRUXodDpsNtu+S7RisRgulwun04nH4wGgqqoKvV7PpUuXpNs3Q7jdbnp7e/f0XI1GQ319PZubm4cS79vb26murk43KflSt7y8vDy6u7vp6Ohgbm6O+/fv09vbm7M5k/tBURRevXpFeXk5N27cyOrrcTgc6e+uZOtYLioq+up7sptw5nA4si6c2d7a8K/6qTBunV8rKrYcYgF/gFAohFarpaSkhGgkum9BrExfhmPGgdvsRtf99U6VEslZQQpiEslXCIfDTExMkJ+fz+joqLwZP2ISiQR2ux2bzUYgEKC8vByj0cjVq1dlWUyOkUgkWF5eZm1tjdraWvr6+g6UOXMSqK6uZnBwkCdPnnDr1q0DhUBPTk4yODiYhdHtjkaj4dKlSywsLPD06VNGRkZOfXj1x50YU49oNPrZTox1dXXpcPr9TmZu3IDOTjCbob09c6/DZoPmZrh3L3Pb/Jzjq7CwkFAoREFBAT09PV8Uvr7EuXPnePr0KUaj8bO/n2p2knJ/RaNRCgsL0ev1NDY2MjAwcGqE9FxCiK2mDfs5vtvb25mcnDy0m7Wmpobbt28zPj6OyWSis7Pzi8dXfn4+58+fp6uriw8fPjA3N0dfX9+JLf8Oh8OMj49z7ty5zzaeyBRCCKamprhx40ZW93MaOS7hzDXvQkko5Bf/6LuZn5dPdXU1iUQCn9dHYWEh8dQqyT4oqigiuBnEPS8FMYlkO6f7TlgiOQTJZJKZmRk8Hg9DQ0NUV1cf95DOBKqq4na7sVqtuFwuCgoKMBgMnDt37sATM0l2cbvdmM1mIpEIbW1t3L1790y49fR6PefPn087xfYzcfd4PGg0mmM5r3R2dlJRUcHDhw+5fv36iWwGkpqEfCx0RSIRotFo+nmpToyph9FoPHQnxi9RWAh/9s/CX/yL4PNtZX4dlmAQFAX+/X8fDvJRpYSvVMB9IBD4bqyF6TLHjx1f8/PzFBQUHErQ3s0lFo/Hd7i/VFVNu78uXLhwIo/Fk0ggENh3XlV5eTmxWIxkMnloIb2oqIhbt24xPT3N8+fPuXr16levGYWFhQwODhKLxXj//j0fPnxgYGDgRGW4bm5uMjU19cWum5lkeXkZk8kkFw+zSKaFs6U/WCISieAP+MnP+67kPj+P/Lx8CgoKqNXVEo1G8fm3FjHKK8r3nCWmydOgQYNn0ZOR1y6RnBZkhphE8hHbA117e3tpaGiQIkwWEUIQDAax2WzY7XaSySQ6nQ6j0YhOp5PugBwlmUyysrLC6uoqVVVVdHV1ZS0QONfZ2NhgeXmZ69ev7/l4ffjwIVevXj3W7pp+v58XL15w6dKlnJpUCiGIxWI7RK7U37evihcVFaWFrtLS0vTfi4uLj/WcLQT8pb8EP/gBtLbCYeaiiQQsLsKP//hWdtiXNIN4PL6jq2MwGEQIsUP40mq1VFRUfPU4DYVCTE1NMTIycuCxCyFwu908fPgQk8mUdn/pdDr0ej01NTVnQjjPRRYWFigsLKRln+1KFxcXATKaQ2i1WpmZmWF4eHhfnXDD4TDT09PE43EGBgZyuiRQCMHc3Bwul4vh4eEjceYmk0m+/fZb7t27J++jThC/9x//HubfM6Nt1aKoCqryXVfi7/6emrYnEgkSyQQI9pVn7Jp3cfHfucitv3grmy9DIjlRSIeYRLINh8PB1NQUJpNJ3kRkkVgsli6DDIVCVFRUYDKZGB4epqhItoPOZbxeL/Pz84RCIVpbW7lz586Zn9Q2NjaSTCZ58eIFw8PDX70xtVqtVFVVHasYBqDVarl16xZjY2N0dHTQ3Nyc9X0KIT4bTp9MJtPPKykpSYtc5eXl1NXVbYUIFxXl/AKFRrPlEFtfh5cvoaXlYHli8TgsL8OFC/Crv/ojMSyRSHxS6iiEoKCgIC18dXV17Un4+hzl5eWEw2GEEHt+vxOJRNr95Xa7UVUVrVZLTU0NBoOB9kzWkEoOhdPpPFDHyJaWFp48eZJRQSzVBOf58+f09PTQ2Ni4p98rKyvj2rVrBAIBpqenARgYGNiXqHYUKIrCixcv0Gq1XL9+/cjOXzMzM/T29sr72BNGXn4eGo2GwsJCCvk0n1RRFCLRyJbjLJnYEoL3cUhpNBo0ebl9DZVIjhopiEkkbK2Gv3v3juLiYm7cuCHt5RlGVVWcTidWqxWPx0NhYSEGg4H+/v4T33HwLKAoStoNVlFRQXd3tywh/ojW1laSySSvX7/m8uXLX8xMmpmZ4fbt20c8wt0pLi7m9u3bvHz5kkAgwPnz5w88YdveifFjd9f2Tozbw+mrqqrSZYzZ7LJ21NTWbjm6/sJfgLEx0Gqhvn5LLPsaQoDLBW63Sl9fnD//521YLB5mZwMZF76+RE1NDR6PZ1f3YMrZm8r+SmWO6XQ6DAYD58+fTwvl8Xicp0+f0tbWlvNi5lkhEokcqDy1oKCA4uJiQqFQRq/d5eXl3Llzh9evX+NyuRgcHNzzsVJZWcno6Cher5d3795RVFTEwMBATpTfhkIhxsfH6evrw2g0Htl+w+EwbrebgYGBI9unJDOU1ZUh1J3FW8lkkkg0QiwWIy8vj9KSUmp1tXjcHiorK/dcMpmitOb4vxsSSS4hSyYlZ5pEIsH79+/x+/0MDQ3ltOX+JCGEwO/3Y7VacTgcqKqKXq/HaDRSW1srJ0UnBJ/Ph9lsxu/309LSQmtr66kPYT8sHz58IB6Pf9Z9sbi4SDKZpKen54hH9mWEEMzOzuLz+bh27donAkuqE+NuZYzbw+lTrq7tJYxlZWVn1kUYDsN/99/Bb/wG+P1bmWK1tbD9aySESiKRJBZL4HKBz6ehtFThD/9hJ//2vx2hoaGSqqqqrAlfn8PhcGC32xkYGCCZTOJ2u3E4HLjdbhRFoaKiAr1en25C8KXz+uTkJHq9ft8dJyWZJxaL8erVqwMHrdvtdhwOR9bElsXFRdbX1xkZGTnQ4qTL5WJ6eprKykr6+vqObYHTbrczPT2971LQTDA2NkZvb69cuDqBvP/N9/zwV39IZWsl0WiUeDxOfkE+pSWln8QBBINBCgoK9tzsS02qeFe8/OT/8ydpvycduxJJCimISc4kQggWFxdZWVnh/Pnz8iY9A0SjUWw2GzabjUgkknZ+1NfXSxHlBKEoCuvr6ywvL1NaWkp3dzc1NTXHPawTxeTkJPn5+fT19e34f0VRePDgQc6VY6fC6SORCKurqywvL9PY2IiiKGmxKz8/f1ehq7S0NKdeS64yPQ3//J8r/M7vqLhcWx3+VFWg0QiE0JCfn0d+fj41NRp+7Mfy+NmfzePixeMZqxCCUCjE5uYmY2NjNDQ0kJ+fn87+qq2t3fc5PeUSu3v3rlwQOWY2NjYIhUIHFuWFENy/f5979+5l7bP0eDy8fv2aixcvotMdrBue3W5nZmYGvV7PuXPnjsyBKoTgw4cPeL1erl27duT3Py6Xi4WFBYaHh490v5LDIYTA6XTy/sF7XvzfXlCiLaFCV0FRcdFnHWCJZIJQMLRn4TPijqAkFX7uBz9Hpel0dgCXSA6CFMQkZ47Uql1zczOdnZ1yMndAFEXB4XBgtVrxer2UlJRgMBjS5U+Sk0UgEMBsNuPxeNJusNNUwnaUCCF48+YNlZWVdHd3p////fv3VFZWHklWV2oc8Xh81zLGL3ViTCaTzM3NMTIyIh0GByCZTO7I+AoEtkod8/PzUdVaNjZ0bG5WsrlZSiymoahoK2esqwsGB8FgONrxKoqC2+3G6XTicrlIJpOUl5ej1+tZWVlheHg4I+d06RLLDd69e0dLS8uhFjomJiYwmUzU1dVlcGQ7icfjjI+P09DQQGdn54G2IYTAYrEwOztLQ0MD3d3dWXWrprIka2pqOHfu3JGLv0IIvv32W0ZHR/fsGpIcH6qqsrm5ydraGoFAAL1eT1NTEw9+8QHW11ZqO77e7MbpdKLT6/ZUNuk2u+n+yW6+/3e/n4nhSySnBimISc4MgUCAiYkJysrK6O/vl+Ht+0QIgdfrxWaz4XA4AKirq8NkMlFVVSVX/U8gqqqm3WDFxcV0dXXJktYMIYTgxYsX1NfX09bWRiwW49mzZxlzyGS7E2MkEmFsbIzz588fafbNSSKZTO7o6uj3+9PCl1arpbq6mqqqKiorK3Nq4SUcDuN0OnE4HAQCAfLy8qitraWuro7a2todQvjKygrJZPLAgsR2pEssN/j222+5ffv2oY7JQCCQ7gyZTYQQTE9PEw6HuXLlyoHFLCEEq6urmM1mWltb6ejoyPh3MhgM8vz5c/r7+zEctar9HSsrK4RCoU/cyZLcIZlMYrVaWV9fJxaLUV9fT3NzM5WVP3JsffjBB374qz+kwlRBYemXFyZ9Pt+eMjhjgRgRd4Sf/K9+ktY7rRl5LRLJaUEKYpJTTzweZ3p6mlAoxIULF3ZcdCRfJhwOp8sg4/E41dXVGI1G6urqzmwm0GkgFAphNptxuVw0NTXR1tYmBeIsIIRgbGyM5uZm7HY7LS0te3JUHKQT4/ZHYWFhRgQHRVEYHx+nrq5uh9PtrPGx8BUIBFBVNS18pQLutVptTglfsPUZejyetPsrkUhQVlaGXq9Hr9dvBTJ/4ViJx+M8f/6cW7duZWQ80iV2vKiqyqNHj7h79+6ht/XgwQNu3759JPcCVqs1LcAdJo9LVVWWlpZYXl6ms7OT1tbWjJwrU+MbGRk5tkZBqZL8b775Rt6f5RjxeJyNjQ02NjZQFIWGhgYaGxs/67xV4gq//Wd/m/Wn69R2f3mRMhaLEY/Hvzi3URUVz4KH7p/q5vt/5/uyy6RE8hFSEJOcWlRVZWFhgbW1tWNdsTtJJJNJ7HY7NpsNv99PaWkpJpMJg8Eg7fcnHFVVsVgsLC4uUlhYSFdXF3q9Xjo1soyqqvzwhz8kmUzy4z/+47t2Ykw9VFUFPu3EuN3hddRlrEIIJicnSSQSX+yeeRpQFAW/34/X6/2i8FVZWZmzE85IJJLu/Ojz+cjLy6Ompga9Xo9OpzuQ8P3tt99y69atjLxm6RI7XtxuN+vr659t+rEfzGYzhYWFtLYejdsk1bGxt7eXhoaGQ21LURTMZjMbGxv09PTQ2Nh4oOMx1TU4EAhw9erVYz0vTE9Po9Vqj6wkX/JlIpEI6+vrWK1W8vLy0iLYXps8OGed/NYv/BaxQIyqls9XYQghcLld6HX6z/7cu+SlwlTBz/zGz1DZIE0BEsnHSEFMcipJrdalrPHyxnt3hBC43W5sNhtOp5O8vDzq6+sxmUxfdQ5ITgbhcJiFhQUcDgcNDQ20t7cfW9et004ymdxV7Ep1nkx9r3YLps/1TozLy8usra0xOjp64rPlUsLX9lLHkyZ8wZbYmnJ/OZ1OEokEJSUl1NXVodfr0Wq1GTmHz8zMUFNTk7HSWekSOz5mZ2eprKw8tKAEW+Lm2NgYd+7cycDI9oaiKLx69YrS0lIGBgYOfXwnk0lmZ2fZ3Nzcd3l4IpHg+fPnaQftcd4vRSIRxsfHpdB8zAQCAdbX17Hb7RQXF9PU1ITJZDpwY4WFf7nAD/+zH5IIJ6huqf6ss8vtdlNVXUV+3s7rlaqoeJe9lOpK+f7f+T5NI00HGodEctqRgpjkVOHz+ZiYmECr1dLX13fiJ27ZIBgMYrPZsNvtJBIJamtrMRqN6PX6nCv3kRwMIQRWq5XFxUXy8vLo7Oykvr5e3igfglQnxo8zu6LR6CedGLcLXdFoFKvVyqVLl3j8+DEXL148sV07HQ4Hk5OTx1oWtB8+J3zl5eWlM760Wi1arTanha8U0Wh0h/sL2OH+ypbQ7fP5MJvNXLlyJSPbky6x4+PJkydcu3YtYyXyT58+5eLFi0feSGdhYQGLxcLw8HBGjvt4PM7MzAxer5f+/n70+t3dNikCgQDPnz9ncHCQ+vr6Q+//sIyPj9Pd3U1t7ddD2CWZI5Wtu76+jsPhoKKigubmZgwGQ8bupxf/1SIP//pDgtYglQ2VFFV8+t0Nh8MAO76HUV+UoD1IdWs13/vr36NxuDEj45FITiNSEJOcCmKxGFNTU8RiMS5cuHAiJmtHRTweT5dBBoNBysvL02WQMjfqdBGJRFhcXMRms2Eymejo6JClrl8h1Ylxt7yuWCyWfl5hYeEnwfRlZWWUlJR8sZRhe8evWCzGkydPuHr1Klqt9qheYkZJlS0NDQ19ddJ4VHxN+Nqe8XUShC/Ycn/5fL60ABaLxSguLk5nf1VXVx+ZmCSE4P79+9y7dy9j+5QuseMhlTGVKSwWC16v91hC3N1uN2/evOHSpUsZE4IikQjv378nEokwMDCwa5fdjY2NdBfeXOio7fF40uORZB8hBE6nk/X1dTweD9XV1TQ3N2c1gsK74uXJrz1h9dEqSkKhtKaU4spi8grz0Gg0KKqC1+OlqqKKmD9G1BuloKSAzh/r5PovXafCcPDcPYnkLCAFMcmJRlVV5ubmsFqtDAwMZLUF+ElBVVVcLhdWqxW3201BQQEGgwGj0SgbCpxChBDY7XYWFhYA6OjowGg0SucFn3Zi3O7u+lwnxu3urq91Yvwa6+vreL1eBgYG0v8XiUR4+vQpo6OjJ1a4TyQS6WYBbW1tR7ZfRVEIBAI7hC9FUU608JUiFoulxS+v1wtAdXV12v113ML2q1ev6O7uzpiQK11iR084HGZqaiqjnSGzIZbuh1gsxvPnz2loaMhIJ9QUoVCIqakpVFVlYGCAysrKjHW8zCRCCB4+fMjw8DClpaXHPZxTi6qq2O121tbWCIVC6HQ6mpubj3RhQlVU1p6uMftbs6w+WiUeiqMm1fT+Q6EQFdoKirXFtH+vnXP/2jkarjXI86tEsgekICY5kQghsFgsfPjwgY6ODtra2s7sSV8IQSAQSJdBqqqKTqfDaDRSW1sryyBPKdFoNO0Gq6+vp7Oz80zdEKuqSjQa3bWM8eNOjB9ndWWyE+OXxnf//n3u3r37SX5IKBRibGyMGzdunNjPTFVV3rx5Q2FhIYODgxl9L78kfFVWVlJVVZUud8yFSel+EULscH9Fo1GKiop2uL9y7bxttVrxer2cP38+Y9uULrGjZXV1lWQySUdHR0a3++bNG1paWtDpdBnd7l4RQjA1NUUkEsm4UOX3+5mamgK2xLfm5ma6uroytv3Dsra2hs/n27HoIskMyWQSq9XK+vo6sVgMg8FAU1NTTiwsh51h3GY37gU38UAcTZ4Gh89B42Aj3aPdlFTLygCJZD9IQUxy4vB6vUxMTFBTU8P58+cPHFZ5konFYthsNmw2G+FwGK1Wi9FopL6+XuamnWKEEDgcDsxmM4qi0NHRQUPD6VsBVBQlLW59XMa4vRNjSuD6uIwxF84JZrMZjUbzWdeC3+/nxYsX3Lp160Q3OZifn8fhcDAyMrLviaiqqp+UOn4sfKUcX7nwmR6UeDyeFr88Hg9CCKqqqtIC2EkQRRVF4fHjx9y9ezdj25QusaMl0y6/FD6fj/n5ea5evZrR7e6X1CJppjMO/X4/jx49Ij8/n/r6evr6+o7dsQlb38lUCexJXBjIRWKxGBaLhY2NDVRVxWQy0dTUdCLO0V6vl6WlJS5dunTcQ5FIThwn9w5TcuaIRqNMTk6iKApXr17NieyGo0JRFJxOJ1arFY/HQ3FxMQaDgcHBwTP1PpxVYrEYS0tLWCwW9Hr9sYQYZ4pkMrmr0BWJRNLh9Hl5eTuELp1OR1NTU853YkyRTCZZXV39YlaPVqvl8uXLPH36lFu3bp1YIbu7u5vKykoePnzI6OjorhMHVVXTji+v17ur8NXU1HTihS/YEq39fn9aAItEIhQWFqLX62lsbGRgYCDn3F97IT8/n/z8fOLxeMayJ4uKitDpdOnMQ0l2CQQCWXG3VFVVEQgEUBTlWM/PDQ0NVFVVMT4+Tm9vb0Y6aa6vr2M2m/ne975HaWkpDoeD8fFxampq6O3tPdYc1rm5Obq6uk7ENTGXiUQirK+vY7VaycvLo7GxkWvXrp24haqqqqp0ub1EItkf0iEmyXkURUm3xR4cHDw2W/5RkiqpsdlsbG5uIoSgrq4Oo9FITU2NXE0/A6SCWxcWFojH42k3WK5OpoUQJBKJHULXXjsxpv7M1dcGEA/FWX28ivODk82pTUL2EEIVFFUWUd9fj65HR/PNZrSNWiYnJ6mtraWx8etdnZxOJ9PT09y8efNEi0E+n48XL17Q09OT/rfP50NRFDQazQ7HV1VV1Yl+rdtJJBK4XC6cTidutxtVVdFqtWn310kVrndjYWGBgoICWltbM7ZN6RI7GpLJJM+ePeP27dtZ2f7c3BylpaU0NzdnZfv7QVEUXr16RVlZGf39/Qc6roQQTE5OEo/HuXz58o5rkxACm83GzMwMRqORnp6eIz+fRaNRnj17xjfffCO/NwcgEAiwtrbG5uYmxcXFNDU1YTKZTvx16cWLF/T29uZEWadEcpKQgpgkZxFCsL6+nl4Fa2lpOdUX/kgkki6DjEajVFdXYzQaqaurO/EXacneicfjLC8vs76+jk6no7Ozk4qK4+0QtL0T48fursN2Ysx1Ip4Ik/90kg8/+EBgIwACNPkaCoq3vpNqUiUZT4KAkqoSmm43oQ6o/MSf+ok9v2a73c7c3Bw3b97MaVEwxXbH13bhS1EUNjc36ejooLOz81QJX/CjvMaU+yscDlNQUIBOp6Ouro6amppT7daIRCK8ffuW69evZ3S7ExMT1NXVSZdYFtnc3MTpdGatG2Qq3D5bgttBWFhYwGKxMDw8vC+3TzweZ3x8nMbGxi/mrW2/R21ubqazs/PIvv8vXrygvb09Zzr95jpCCLxeL2trazidTiorK2lqasJgMJyIa+5esVgs+P1+ent7j3soEsmJ4vTcqUpOFW63Ox24e1rzEZLJJJubm9hsNnw+HyUlJRiNRi5evHgi8gokmUMIgdvtxmw2E4vFaGtr45tvvjmSGzUhRDqc/uMyxkQikX5ecXHxDqFLp9NRVlZGUVHRiRW7vsbKwxWe/p2nuOZcFJYVUtVcRX7R7ucioQoi7ghT/78pKu5X8Dr+mgt/6kJaOPsSBoOBZDLJ2NgY169fz5n3U1VVgsHgDuErmUzucHw1NDTQ29ubLvlUFIWXL1/icDiora095ldwOJLJ5A73l6IoVFZWotfr6e/vp6ysLGc+q6OgtLSUWCyGECKjr7u3t5enT5/K7rhZxOl0ZlU8SXXkjUajOZGvBdDZ2UlNTQ2PHz/m8uXL1NTUfPV3vF4vr1694uLFi1+tRtBoNDQ3N9PU1MTy8jIPHjygvb2dtra2rF67vV4vyWRSimFfIeWyX1tbw+v1UlNTQ1NTU8abwOQSBoMBs9ksBTGJZJ9Ih5gkp4hEIkxMTKDRaBgcHDxVwpAQAo/Hg81mw+FwoNFoqK+vx2g0UlVVdWov0JLPk0gkWFlZYXV1lZqaGrq6ujJqdd+tE2PqoShK+nnbOzFuL2XMdifGXEUIwcT/d4Lxvz9OIprYEsIKvy7KJxIJ/AE/xfFi4sE4Hd/v4Ht//XsUV+7NnbCysoLNZmN4ePhI3/evCV9arTZd6riXrDMhBDMzMwSDQa5evXoiVuCFEIRCIZxOJw6Hg1AoRH5+PjqdDr1eT21t7alyux2UycnJtHM5k0iXWHZ59OgR169fz+oxvL6+TjAYzLnJeMq99jXH1+rqKktLS4yMjBxI1FNVlYWFBVZXV+nu7qa5uTnj53EhBI8ePeLKlSsZbRxwWlBVFZvNlj4W6+rqaGpqorq6+szcyzx69IjR0dETm0sqkRwHUhCT5ATJZJIPHz7gcrkYGhra00reSSAUCqXLIBOJBDU1NRiNRvR6/al0vUn2hsfjwWw2Ew6HaW1tpbm5ed/Hw/ZOjB+XMu7WifHjzC45uf88U/9sisd/8zF5BXlUmvYuULrd7nQwfDwUx7/hp+vHu/j+//37n3WWfYzZbMbn83H58uWM38B/SfiqqKjYkfGViZvptbU1FhcXGR0dzbmAYkVR0u4vl8uFoiiUl5dTV1eHXq+nvLz8zEyg9oPL5WJ9fZ0LFy5kdLsySyx7CCH49ttvv9jkIxOoqsqDBw/43ve+l9X9HAQhBFNTU0QiEa5cubLjeiuE4N27d6iqysWLFw8t4CeTSebn57FarfT29mIymTJ2TG9sbKTvkyVbJJNJrFYr6+vrxGIxDAYDTU1NZzZHy2w2U1RUREtLy3EPRSI5MUhBTHKsCCFYXV3FbDZz7tw5GhsbT/TNcCKRwG63Y7PZCAQClJWVYTKZMBgMOTchlBwtqc6DKysraLVaurq6qKqq+uxzd8vr2q0T48eZXaWlpVJsPQSO9w7+xb/7L1BiCtpG7Z5/LxaLEY1Gd3ym8VCcoC3I9V++zuV/9/KetzUzM0MikTjwpCeVdeX3+9PCVyKR+ET40mq1We+S5na7efPmDcPDw8c2QRFCEA6H09lfgUCA/Px8amtr0ev16HQ6KRDvESEEDx484N69exnftnSJZQe/34/ZbOby5b2fgw7Kq1ev6OjoyNlFTYvFwuzsLMPDw5SXlxOLxRgfH6e5uZn29vaM7isej/Phwwfcbjd9fX3U19cfanuqqnL//n3u3r175s9XsViMjY0NNjY2EEJgMploamo6VVUlByUcDjMxMcHo6OhxD0UiOTFIQUxybDidTqampjAYDPT09JzISbyqqrjdbmw2G06nk/z8fAwGA0ajkcrKyhMt7kkyg8/nw2w2EwgEaG5uxmQykUgkPilhjEaj6d/5uBPj9nD6k1B+dlJR4gq/9e//FhvjG9R21e75+ysQuJwuampryM/beR4LWAPkF+bz07/+0+h79575MjExQWFhIefPn//8foX4xPF1XMLXlwiHw4yNjdHf34/BYMj6/hRFwePx4HA4cLlcJJNJysrK0p0f5bn5cIyPjzMwMJDxki3pEssOi4uLFBQUHIljxOPxsLS0dCTi20EJBoPpEsr19XUuXbqU1bzDaDTKzMwMgUCAgYGBA+9rdnaWwsLCL5Z9nmbC4TDr6+tYrVby8/NpbGyksbHxWK9tucqDBw/keVQi2QdSEJMcOaFQiImJCQoKChgcHMyZANa9EggEsNls2O12kskktbW1mEwmdDqdFCvOMNs7MQYCAVZXV9nY2ECj0aDVatMrl6lOjB+7u05yJ8bTwuIfLPJ7v/h7VBgrKCzde8lgJBIhqSSprPjUASWEwD3vpv/n+rn3V/buqhFC8ObNG7RaLZ2dnbsKX8AnpY65OjlIJpOMj49jNBrp7OzM6La3u7/8fj95eXk73F8ySyWzrK2tEY1G6e7uzvi2pUss84yPjzM4OEhZWVnW9yWE4P79+0fWFOagLC4u8uzZM/r6+rh48eKRXHvD4TDT09PE43EGBgY+6xDfjVgsxpMnT7h3796Zuk/w+/2sr6+zublJcXExTU1NmEymM++Q+xoTExM0NDTIxgsSyR6RgpjkyEgmk7x//x6v18uFCxf2dTNwnMTjcex2O1arlVAoREVFBUajEYPBkLOTT0lm2d6J8eNSxu2dGIUQ+P1+EokETU1NdHR0pN05Z+km9qTyO//X32HpD5ao7dr7Cr4QAqfLiV6n/+xnHHKE0ORp+Lkf/Bzl9Z931ezm+FpaWqKiooKmpqYTIXx9CSEEExMT6ayeg3wnVFXF4/GkBbBEIkFpaWna/aXVauV3LcskEgnGxsa4fft2xrctXWKZ58GDB1nPD9vOhw8fqKyspLGx8cj2uVdUVeXt27doNBouXLjA0tISFouFkZGRIzunBgIBpqamyMvL27PT8tWrVzQ3Nx+67DLXEULg9XpZW1vD6XRSWVmZft25LLDmGtnKepRITitSYpdkHSEES0tLLC0tcf78+ZxveayqKk6nE6vVisfjobCwEIPBQF9fHxUVFcc9PEmGUVU1LW59HFKf6sSo0WjSnRhLS0vRarUYjcZ0Xtf6+jrLy8uUlZUxODj41Xbtktwj7AxjeWmhtHZ/GSShUIjysi8HsJfWluJd8rL2dI3eP9qb7mi4XfiKx+PAjxxf9fX1dHV1cf36dZ49e5YOCj7JpCahi4uLPHnyhNHR0a+u9EcikbT45fP5yMvLo7q6Gr1eT3t7+4kUBk86hYWFCCFIJpMZd2oUFRVRW1uLzWaTLrEMEI/Hj9wh2dbWxqtXr3JOEItGo4yPj9PW1kZraysAnZ2dVFdX8+jRIy5fvnwk2WeVlZVcv34dr9fLmzdvKCkpob+//7P5V36/n2g0emrFMCEEDoeD9fV1vF4vNTU1NDc35/xcIZepra1lYmLiuIchkZwYpCAmySqbm5tMT0/T0NDAvXv3cnKFJ+XqsdlsbG5uoqoqer2epqYmhoaG5AX5BKMoyq7B9F/qxFhTU0NDQ8NXOzEGg0Hev3+P2+2mubmZ69evy8n5CcY17yIejO8rSF9VVaLRKDr97gKoQKAkFRLJBLFYjDd/8AZ7jR2A8vJyqqqq0Ov1dHZ2frHpxujoKE+ePKGgoACj0bi/F5aDdHR0UFFRwcOHDxkdHU2XcqmqitfrTQtg8XickpKStPhVVVUlz8c5gsFgwG63Z0X06O3t5enTpxiNRvl5HxKn03nkZVMlJSWoqkosFsuZZkKp5h5Xrlyhurp6x890Oh23bt1ifHw87ew+Cqqrq7l16xZOp5MXL16g1Wo5f/78J+/Zu3fvuHTp0pGM6ahQFAW73c76+jqhUAi9Xk9HR4c8x2cIjUZDWVkZ4XD4SEqlJZKTjiyZlGSFYDDIu3fvKC0tpb+/P2duilJEo9F0GWQkEkk7fgwGg8wmOCFs78S4Xej6XCfG7XldB+3EqKoqGxsbLC0tUVRURFdXFzqdTt7AnQKm/qcpvv0r31LTWbPnz9Pn91FcXExxcTGKopBIJEgmkiSSCYS6dQzm5+dTUFhA1BqlcaSRP/b//mMHGp+iKDx+/Jj+/v5Tkwvicrl49OgRdXV1JJNJgLT7S6/X59x1Q/IjAoEAMzMzDA8PZ2X7ExMT1NfXnwoB+Dh59+4dzc3NWQ2N343V1VVisVhWcub2y+LiIuvr64yMjHzxnCKEYHJyklgsxuXLl4+80ZPdbmdmZga9Xs+5c+coLCzEarVit9u5ePHikY4lGySTSSwWC+vr68TjcQwGA83NzbLyIktkM+tRIjltyJm/JKMkEgmmp6cJBoMMDQ2h1e7dbZFNFEXB4XBgtVrxer2UlJRgMBgYGhqSqyc5hhBiRxfG7e6u7Z0YCwoKdohcBoMha50YQ6EQCwsLOJ1OGhsbGR0dlW6wU0Y8FAcNexbD4vE4Pq+P0tJSQnmhtPBVVFREWXnZJ90m1RIVJaQceHz5+fncuHGDx48fc/HixSMp7ckkqWyYlPsr5R45d+4cq6urnDt3jra2tuMepmSPVFZWEgwGEUJkZUEg5RIzGAxyweEQeL1eBgcHj3y/jY2NPHr06Fgn46qq8ubNGwoKCrh9+/ZXjyONRsPQ0BAbGxs8fPiQ4eHhjHdS/RIGg4H6+nosFguPHj3CZDKxvr5+pPlvmSYWi7GxscHGxgZCCBoaGrh06dJny0MlmcNoNDI2NiYFMYlkD0hBTJIRhBAsLCywurpKX1/fsa/qpiZfNpsNh8MBQF1dHW1tbVRXV8sb7GNCCEEsFvskqyscDqczlGArR+bjMsaysjKKi4uP7LMTQmCxWNIt6zs7O2WmxSlGk7f3zzUej+PxeigtLaWwsJBkMkleXh7FRcUUFBag4dNtCSHQ5B/u2CksLOTGjRs8efKEq1ev5syCw27EYjFcLhdOpxOPxwOQLhG9dOnSju7C586d4/Xr1wSDQfr7++V37IRQU1OTzvzJNKksMbvdfuz3EyeVVCzAcURV5OfnU15ejs/nO5YGStFolLGxMTo7O2lubt7X7zY2NlJVVcX4+PiR389qNBoaGxtpaGjg6dOn+Hw+VlZW6OjoyMnIkd0Ih8Osr69jtVrJz8+nqanpSJsWSLbIZtajRHLakCWTkkNjs9l4//49LS0tx3rRjkQiaXt5LBajqqoKk8lEXV3dkVvfzyJ77cRYXFy8o4wx5fLKlU6M4XCYxcVFNjc3MZlMdHR0yNKtM8Dsb8/y+7/y+9R21n5RHItGo4RCISq1lUTCEaqqqhAI4vE40UiURCJBYVEhpSWlFBYVpsUx94Kb7p/o5vt/9/uHHmskEuHp06eMjo4eqYPhc6RyGJ1OJw6Hg2g0SmFhYbr0saam5qvXBSEEc3NzeDwerl27Js/ZJ4DNzU0cDgf9/f1Z2b7sOHk43G43a2trx9Zpzul0sr6+fuTlfk6nk3fv3nH16tVDiXGKovDy5UsqKiro6+s70mMwHo/z+PFj7t69y/LyMsvLy3R1ddHS0pKT3wW/38/6+jqbm5uUlJTQ1NSE0WiUQswxMzs7S0VFRc41uJBIcg0piEkOjN/vZ2JiIn2zcNSrP8lkks3NTWw2Gz7fVumS0WjEaDTucB9IDs9BOjFuF7yOusvVfhFCYLPZWFhYQKPR0NnZKUt1zhiOGQe/+Sd+k9KaUorKdz+Xpcp2a2pqUBSFYChIdVX1jucItkp+I5EIiXiCgsICSktKCa2FuP7L17n8713OyHhDoRBjY2PcuHHjyMtP4vH4DveXqqpp95derz/UeCwWC3Nzc4yOjsrzeI6jqioPHz7MakmXzBI7OHNzc1RUVNDQ0HAs+xdCcP/+fe7du3dk19KFhQUsFkvGHEmp6gebzcbw8PCR3ee+efOGhoYGDAYDsCXOzc/PY7FYOHfuHA0NDcd6fyKEwOPxsLa2hsvlorKykubmZurr60+Mk+0skGr+lK2sR4nktCCle8m+icfjTE1NEYlEuHjx4pEFYgohcLvd2Gw2nE4neXl51NfX09nZiVarleLFAdmtE2Pq39tLLlJiV6qEsbGxkdLS0hO9AhiNRtM3u0ajkStXrshsizNKTXsNZboyop7oroJYMBgkmUxSU1uDBs3W+WaX5SQNGooKiygqLEIgSCaSBH1BwtEwm+omVqsVg8Fw6ElDeXk5165d49mzZ9y8eTNrLkYhBIFAIJ39FQ6HKSwsRKfTYTKZ6Ovry6ibK9Xh9cmTJ7t2hJPkDnl5eRQVFRGNRrMmXsossYPjdDppbW09tv1rNBoMBgM2mw2TyZTVfSmKwuvXrykpKeHWrVsZO1Y0Gg1dXV3U1NTw6NEjLl++nPX8xkAgQCgUSothsFWC2tvbS1dXF7Ozs8zPz3P+/Pkdz8k2QggcDgdra2v4fD5qa2tlN/Ycp6KiglAolLWsR4nktCAdYpI9o6oq8/PzbGxsMDAwQH19fdb3GQqF0mWQiUSCmpoaTCYTer1erkLtgY87MW4Pp/9cJ8bUo6Sk5FSWLQkh2NzcxGw2I4Sgo6MDk8kkbxYkjP39MV7+w5fUdtemjwfBVjmgBg2V2sp0CaSiKPgDfmqqvz458q54qWmv4cd//cex2rfOZ6myEpPJdKjvmcfj4e3bt9y6dSsjTsxkMpl2f7lcLlRVpbKykrq6OvR6/ZE1IUllAPX09Bybw0XydZaWlgBob2/P2j6kS+xgPHjw4NgD2cPhMO/eveP69etZ20ckEmF8fJyuri6ampqytp9YLMb4+DjNzc1ZPd6fPHnC4ODgFzMiY7EYMzMz+Hy+rHYeVhQFu93O2toa4XCYuro6mpqaqKqqkvdMJ4S3b9/S2tp64hrxSCRHiRTEJHvCYrEwMzNDe3s77e3tWbsQxuNxNje3XBSBQICKigqMRiMGg0HmOG3j406M211du3Vi3J7Vla1OjLlOLBZjcXERi8WCwWCgo6NDdhiV7MBtdvOb/+ffRIOG8vrydHOOwqJCKsp3OmEVVcHv83/1JjMZS+Jf83PrL9/iws//KMsnHA6ztraGzWajoKCA5uZmTCbTgUQtp9PJ9PQ0t27d2pe4JoQgGAym3V+hUIiCggJ0Oh16vZ7a2tpjFcUVReH58+fU1tbS09MjJ2A5SCwW4+XLl9y8eTOr+3j27JnMEtsH4XCYyclJRkZGjnsoPHz4MGudmR0OBxMTE1y7du1ImowIIZicnCQWi3H58uWMnx/tdjsbGxtcvry30vpIJML09DTRaJSBgYGMOGoTiQRWq5X19XXi8ThGo5GmpqYjqwaRZBaHw4HdbmdgYOC4hyKR5CxSEJN8EZ/Px8TEBFVVVfT19WW8PE5VVVwuFzabDZfLRUFBAQaDAaPRSEVFxZm8+U11Yvw4mH63TowfC11H3Ykx10lZ/BcWFkgmk2k32FkTAyV7Z/z/Mc6L//oF2mYtgXCA0tLSXctoVVXF6/NSW1P72W0JIXAvuDEOGfnpX/9pCst2F7ui0Sjr6+tYLBby8vJobGyksbFxXxNIm83G/Pw8N2/e/OzxnUwmcbvdafeXoihUVFSks7/Ky8tz7twhhEhP+C5fviy/uznIgwcPuHPnTlY/G+kS2x+rq6skEgk6OzuPeygsLy+jKEpGxyKEwGw2s7m5yfDw8JHnlG5sbDA3N8fw8HDGGpukMtdu3bq1b/EwFAoxNTWFqqoMDAxQWVm5r9+PxWJsbGywsbGBEIKGhoZ0LIbkZHMUWY8SyUlHCmInECEE7x3vmXXNMu+axxF2IIRAW6yls7aT7tpuLhovUlxwcEdVNBplcnKSZDLJ0NBQRi/4gUAAm82G3W5HURT0ej1Go5Ha2tpTP9kRQnw2nD6ZTKafV1JS8kkwfS51Ysx14vE4S0tLbGxsoNfr6ezszIlufJLcJx6K84M//QOWx5bRdesoK9/dRagKFa/HS23t7oKYEAL/mp+C0gJ+6h/+FKZLe8vQicfj6YmJqqo0NDTQ1NS0p4ym9fV1VldX0+VJ4XA43fkxGAySn5+/w/11kvL/VldXWV5ezprTRHJwpqen0ev1Wc0zki6x/fH69Wu6urqOxDX1NZLJJI8fP87YhPw4uz9uJxAI8OLFC/r6+jIi1C4sLKAoCj09PQfehs/nY2pqisLCQgYGBr7ogv/YpdzY2EhDQ4M8v55Cnj17xqVLl2SjGonkM0hB7AQRV+L87vzv8pszv8nU5hThRBiAPE0eGo0GRVXQoKEgv4AWbQs/c/5n+KO9fxR92d6zBVRVZXZ2Nm2vzUQuQSwWw263Y7VaCYfDVFZWpssgc7374H7Y3onxY3fXbp0YP3Z3nfj3QlVhYwP8fhACKiuhqQmOqORKCIHL5cJsNhOPx2lvb6exsfHUi6ySzBKJRLj/g/s4/nsHgaUAVa1VFBR/KhwJIXB73OhqdZ/+TBX4Vn0UlBRw5z+/w7l/7dyBxpJMJrFYLKyvr5NIJDAajTQ3N+86yVEUBbfbzfv377FYLGmXbcr9dRocty6Xi7dv3zI8PLxvB4Qke3i9XhYXF/dc5nVQpEts7zx48CCnxMPx8XH6+voO/b0Nh8OMj4+nOy0eN8lkkpcvX1JZWXkocS6RSPDw4UPu3buXkXsWt9vN9PQ05eXl9PX1pYUQv9/P2toaDocjYzmWktxnZWUFRVHo6Og47qFIJDmJFMROCLPOWf7W47/Fc8tzAOrK6qgo+nSCI4QgpsRwhBxEk1Haa9r5pdFf4g91/KEvXqiFEGxsbDA7O0tnZyetra0HvrArioLT6cRms+HxeCgsLMRoNGI0Gk+sSyfViXG3Msbt4fQpcetjd9epvdmIxeDhQ/iX/xLevQOPBxKJrZ8VFEBVFQwNwR/6Q3DvHmQhsyuRSLC8vMza2hq1tbV0dnbKybLkQPj9fl68eMHw8DCKW+EP/vIfYH1tpVhbTHldOZq8H50TBQK3y41Ot1MQiwViBK1BKkwV3PpLt+j68a6MjE1RFGw2G2tra0SjUaqqqigtLSUcDuP3+8nPz6empga9Xo/X6yUcDnPp0qWcmRBnilAoxPj4+JE1dpF8nVSp1/e+972s7ke6xPaGoig8ffqU27dvH/dQ0mxubmKz2RgaGjrUNqamprh27VpOXeNT5Zt2u53h4eEDOazevXtHXV1dxkU+u93O69evURSF4uJiampqaGpqor6+Xi4WniHi8TjPnz/n1q1bxz0UiSQnkYLYCeBfLvxL/sq3fwVn2EmztpnSwr3V9CfVJGu+NQryCvj5Cz/PL478Ivl5nwozHo+HiYkJdDodvb29+y6jEULg8/mw2Wxsbm4ihKCurg6j0UhNTU3O37imwuk/dndt78SYn5+/q9BVWlp6Nm8qhIDf+R34h/8Qlpa23GHl5VuPlNMtkYBwGEKhrX83N8O/9+/BH/tjGXGNud1uzGYzkUiEtrY2mpqaTq/wKMk6brebt2/fcv369XRuSiKS4O2vv+Xdb7wj6o1SWFpIcVUxhWWF5OXn4XK5qK2tJRlNEg/GiXqjFBQX0HK7hRu/coPq1uqMjE1V1R3ZX/F4HEVRSCQSaDQaGhsbaW5u3tH5a2ZmhmQyyeDgYEbGkEskk0nGxsZoaGiQK945wosXL+jt7c26UCFdYl9nc3MTh8NBf3//cQ8lTUo0vXfv3r7vCYUQzM3N4XK5GB4eztlSb6fTybt377hy5cq+wu1DoRCvX7/OmICZyk5dW1vD5/NRW1tLUVERNpsNo9FIT09Pzr6Hkuzx7bff7rvxjkRyVpCCWI7zrxb/Fb/6B79KJBmhparlQOKSM+zEH/PzCxd/gV+6/kvpbUQiESYnJxFCMDQ0tK/wzEgkks4Bi0QiVFVVYTQaqa+vz5kLrRCCeDy+axnjlzoxpoSus9iJcU+43fC3/hb87u9u/dtkgq91AI3HwWaDZHLLKfaf/qdwgAlNMplkZWWF1dVVqqqqciYjRXKysdlszM7Ocv369V1X973LXuZ/d+HG0EMAAQAASURBVJ7ZH8wSdoZJhBOg2SrfKS0tpaC4gKLyIlq/aeXcv36OxmuNO9xk+yUSiaQ7P/p8PvLy8tLuL51Ot2OMqcnP+vo6Xq8XnU5HU1MTtbW1TE5OUlhYyPnz5w88llxFCMHbt2/Jy8tjaGgo5xdeTjsWiwW/309vb29W9yNdYl/n/fv36PX6nHNQTk5OUl9fv6+suVRJYlVVFb29vTn/mUejUZ4/f05zczPt7e17+p2nT5/S399PVVXVgfebchCvr68TDoepq6ujubkZrVabfs+EEKyvrzM3N0dLSwudnZ3yHvcMMTMzQ01NjVxMkEh2QQpiOcyyd5k//YM/jSvsorX64CWMAK6wi1AixN/8P/1Nfqzjx/jw4QNOp5PBwcHPhkJvJ5lM4nA4sNlseL1eSkpK0mWQx9GFRnZiPCacTviP/2N4/nxL0NqvGBUMgsUCg4PwD/4BNDbu6de8Xi/z8/OEQiFaW1tpaWmRq1ySjLCyssLa2hrXr1//6jGlJlV8qz7cZjcRd4SZ6RkGrw5S015DTWcNReX7L5VRVRWPx7PD/VVSUoJer6eurm7HhOZrCCFwu92sra3hdrupqqoiGAzS0NBAd3f3vsd2ElhYWMBmszEyMpIzizFnEUVRePLkCXfu3Mn6vqRL7Ms8evSI69ev59z3IRgMMjU1xejo6J6enyqPzlRo/VEhhGBiYoJ4PM7ly5e/eF1xOBysrKxw9erVfe8nkUhgsVjY2NggkUhgMBhobm7+ajSJEIKlpSWWlpbo6Oigra1N3g+fAfx+P/Pz81y5cuW4hyKR5By5dbWUpFFUhV978mtYg1a6arsOfbHSlekIeAP8rQd/i7A5zMjAyBcDQIUQeDwebDYbDocDjUZDfX097e3tO8pyssFBOjGWl5dTV1eXDqeXF/csEIvBX/gLW2JYa+vXXWG7UVEB7e0wOQm/9Evwj//xVvj+LiiKknaDVVRU0N3dva8yBInka8zNzeH1erlx48aeVsrzCvKo6aihpqMGAPdDN7139ueIiUajO9xfQNr91dbWRvFBvlffodFo0Ol06HS6dCn76uoq4+PjLC4ucuHChVOXHdPZ2UlFRQUPHz5kdHT0i13VJNkjPz8fjUZDIpHIeoOYc+fO8ezZMwwGg7zWf4QQAkVRck4MA6ioqCAWi5FMJr86PpvNxvv37xkeHqaiouKIRpgZNBoNFy5cYH19nYcPHzIyMrLreUkIwdTUFDdu3NjztmOxGOvr61gsFgBMJhOXL1/eV/dAjUZDR0cHra2tLCwscP/+fbq7u2lqapLfp1NMZWUlfr8fIYT8nCWSj5AOsRzl0coj/tzv/Dl0ZToqig5/MxCPx/H6vGwmNvnFG7/In7/+5z95Tjgcxmq1YrfbicfjVFdXYzKZ0Ov1GXPj7NaJMfVQVRXYvRNjyuF14jsxnmT+8T+Gv/t3t1xdh3UFxuOwvAx/5s/AL//yjh/5fD7MZjN+v5+WlhZaW1tz8uZecnIRQuwoFz/ozeHDhw+/6IhRVRWfz5cWwGKxGMXFxenOj9XV1UdyY5rK7ykqKiKZTFJSUkJzczNGo/HUOC1TDREuXbq0J9ezJPOYzWaKi4tpbm7O+r6kS2x3ct0Fsri4iEaj+Ww5oRCCDx8+4PV6uXbt2om/9gcCAV68eLGry21paYlYLPbVMuNQKMT6+jo2m42CgoJ0Z8iDhPfvRjKZZG5uDrvdTm9vL0ajUQomp5RXr17R1dV1qPJcieQ0IgWxHOVX/uWv8Ntzv0237nBlLoqi4Pf70Wg0aCu1WENWaktr+Rc/9y8o0hSlO//4/X7KysrSZZAHcSkkk8ldxa5IJLJrJ8aPSxlPy8TsVDI/Dz//81sZYJmagDidW8H7/+1/izI0xNraGisrK5SWltLd3U1NTU1m9iORbEMIwcuXL9FqtZw7d+5Q2/pYEIvFYmnxy+v1AlBVVUVdXR06nW5fq/iZRlVVnjx5Qk9PDxUVFaytrWG32yksLKSpqYmGhoYTP/mMxWKMjY3R0dFxJKKMZCfhcJiJiYk9l8QdBpkltjuLi4sUFBTQ0tJy3EPZlUQiwdOnT7l79+4nP0smk7x48YKamhrOnTt3aj7XVA6aVqvl/PnzaDQakskk3377Lffu3dvVsevz+VhfX2dzc5PS0tIjWcCIx+N8+PABj8dDX18fdXV1WduX5Hiw2Wx4PJ5TmSsqkRwGKYjlIM6wkz/yT/8IeZo8dGW6A21DqIJAMEAikUCr1VJYWIgQgnA0zIp3hT/X8ee4prtGfX09JpOJysrKL958pDoxfpzZtVsnxo+FrjPbifE08Xf/LvyjfwTd3ZCpm1QhUObmcNy9y/s//sfTbjDpApRkC0VRGBsbo7Gxkba2tkNtSwjB7//+79PR0YHT6SQajVJUVLTD/ZVr571kMsmTJ0/o7+9Hr9cDWwH+GxsbWCwW8vLyaGxspLGxMWPug6NGVVVevnxJRUVFevIpOTru37/PN998cyTvu3SJfcrz588ZGBjI6dLhZ8+eMTQ0tCPrKuWk6u/v31fo/klBCIHZbMZutzM8PMzs7Cw1NTU0NTWlf+52u1lfX8flcqHVamlubqauru7IryPRaJT3798TCoUYGBiQi5OnCFVVefTo0a6CtERyljnZy8GnlHnXPMF4kEbt3gLHdyAgHAkTDoWpqKigrKyMWDRGIBBAqILCokLy8/MpaSzh9rXb6U6MXq/3E3dXLBZLb7awsHCHyFVdXU1ZWRklJSVywnHaCQTgf/vftrK+MvBZCyGIRqOEw2GKiorQvX3Lvb/219DISY0ki8TjcZ49e0ZPTw8mk+lAv+9y/f/Z+8/YSLs+vQ+8KjDHIisH5hyabKZi6vRYIwkja4DxYAB5Bhp9WHsE24Ihy5qVhLGx+0VrryxhPiwkyzIWMFbQLuQBrBmM7PFI7zvdzWaqYmYzZ7Jyzrnu++wHvlVvs5vNZqhwV/H8gAfP83Sz6j6susM517n+198Fh8MBr9ebzlksKyvD0NBQXpqLPBShUIjp6WnMz8/j5cuXqK+vR0VFBTo6OtDR0YFYLAaTyQSdTgdCSFocy6ez7aHw+XyMj4/j8PAQer0e4+PjnBMmi5mGhga43W40Nj5uM+8h0CyxbwmHw5wWwwCgtbUVFxcX6O/vB3DdofTg4ABarfaHgfCFCo/HSzvf379/D0II+vr6YLPZYDQa4fP50NDQAI1Gk/euueXl5RgZGUE4HMbOzg4SiQQGBwdpR+8igM/nQygUpuMbKBTKNVQQ4yBnnjMwLIMS/sOcMvFYHH6/H3wBHwKhAMFQEAK+AMISIcrLy0HY67DVRDyBP9/6c/SHrycjX3ZirKysRGNjIyoqKmgnRso1e3vX5Y2PEBG+JJlMIhQOIxGPo7yiAiKRCPz6euD8/DpknwpilCwRiUSwtLSEoaGhey3UCSHw+/3p8sdIJIKSkhKIxWKoVCoMDAyAz+djbm6u4ErzSkpKMD09jYWFBYyNjd1Y5JSVlaGtrQ1tbW3pDmZra2tIJpNQKBRQq9WcX2wD14vPnp4emEwmfPr0CZOTk3TynyNUKhVMJlNOBLGysjI0NDTAZrNRlxiuRftCcFjLZDLs7e2ht7cX+/v7CAaDePPmzbOIzRCJROmu7X/yJ3+S3ox4SDfhXFFZWYmJiQkEAgHs7OyAz+djYGCgaEXL54JCoYDVakVzc3O+h0KhcAYqiHGQQDwAHo93/4cjAQxGQ7qzkEAggFAovN4V510v7gghEAgFKC0rRQ2pQUVDBbXMUu7Hycl1dtgjSqgIIYjGYgiHQuDx+aiqqrqe+N12jF/5lYwMl0L5kkAgkHYKfW+HO5FIwOVywel0wu12g2VZ1NbWQiwWY3BwsCBEoIdQVlaGqakpLC4uYnJy8tYFTklJCZqbm9Hc3AyGYWCxWLC9vY1oNAqZTAaNRsP57m8qlQqVlZWYn5/H2NgYDRLOAY2Njfj8+XPOjkddYr/E6XSmS6G5DI/HQ319PX72s5+htbUVExMTRf3dpTYXjEYj/H4/WJbFb/zGb+D09BTRaPSHkSX5pqamBlNTU/B6vdjY2EBFRQX6+voKwhVN+RalUomNjQ0qiFEoX0AFMQ7C+1YuuBMCAqFQCHGj+LqEkX/363k8HgT84t+Jo2QIsxkg5EHlkgzDIBQKIR6Po7y8HPUiEQR3lS0ZDBkYKIVyE4/Hg42NDUxNTaVFLUIIgsEgHA4HnE4nwuEwhEIhGhsbIZPJ0Nvb+yycChUVFZicnMTy8jKmp6fvXNwIBAKo1Wqo1WqwLAubzZbOmJFIJNBoNJwVm0QiEaanp7G8vIze3l7qJMoyPB4PFRUVOSvdoy6xX+J0OqFSPSJqI8f4/X7YbDaUlpais/NpjaO4SjQaTWczAtcixMjICHQ6HSYnJ1FeXo7h4WEYjUbMzc1hYmKC8xsv9fX1mJ2dhdPphF6vR319PXp7ews2b/K5Ul5ejlgsBpZlaZwAhfILqCDGQerK69KurvvsGvF4PMhkMrjdbghLhD8stUwwCUgqafcYyj1JJh/04/FEAoarK9TU1KCurg6lZWV3S7x8PvBFXh2FkglsNhv29/eh1WoRCARwfn4Ot9sNhmFQU1MDsViM/v5+VFZWcnp3PptUVVVhfHwcS0tLmJmZuVdZIZ/Ph0KhgEKhACEEDocDJycn8Pl8aGxsRFNTE+rr6zn1mVZUVOD169fQ6XQIBAJFuwjnCkqlEmazGR0dHTk5HnWJXePxeDAwMJDvYdyJyWTC0dER3rx5A71eD4ZhimYDIhQKwWg0wmq1QigUQq1WY3JyMl3GenV1BalUeiOTUa1Wo66uDsvLywXTUEAsFuPNmzewWq1YWFiAVCpFd3d3wXcpfk6IxWI4nU5IpdJ8D4VC4QT07sVB2kXtEAqEiDNxlAnvl3vC5/PRILoOs60X1d/5YCIg6BZ3Z2q4lGLngZkkQoEAFRUVSDIMnC4X+Hw+ykpLUVZejvKysm8nvwwD0HwfSgYghCAUCmFvbw9nZ2eQSqVYX19HY2MjJBJJxiftxdCkuba2Fi9fvsTi4iJmZ2cflEHE4/EglUohlUpBCIHL5cLFxQU8Hg9EIhE0Gg0aGxs5IVIIBAJMTU3h8+fPWFtbw8jICCfGVYzI5XLodLqcCWLUJXbdPQ4AZx0fhBDs7u4iHA7j9evXadep0Wgs2NKtVNak0WiE3W5HZWUl1Go1Zmdnv5nnMAyD4+NjvH379pv3qampwevXr7GysgKXy1Uw3XHlcjlkMhlMJhPm5uagUqnQ0dFRNAJnMaNSqXB5eUkFMQrlF1BBjIN0NnaiprQG/pgfEuH9nVx8AR+iBhE8bg9EDaJbH0qxZAwCngBdjV2ZHDKlmPlFW/D7lk3y+XyUlpWh9BcLa75AgEg4jGgkglg0CpZlUVJSgrLycpSVll5P/FpasvgLUIoVhmFuZH8lk0mEw2EAwK/+6q9yPpuFK4hEIgwMDKRFsccsaHg8HsRiMcRiMQgh8Hq9MBgM2N7eRm1tLTQaDSQSSV4X7DweDy9evMDFxQXm5+dvuDcomaO0tBQsy+bU/fPcXWI+nw/19fX5HsatxONx6PV6yOXyGw62pqYmLC8vF5QgRgiB2+2GwWCA2+1GXV0d1Go1ent777y3HRwcoLOz87vXg1AoxOTkJI6Pj7G4uIjx8fGCKEXk8XhQq9VpgeXDhw9oaWlBa2srZ8VZynX56+bmZr6HQaFwBiqIcZD68nr8B63/Af5w7w8hqXpYaaNAIEC9qB4etwcNDQ3gC24+kOwhO5rqmjChmsjkkCnFTHv7daB+LAZ8YfW/i9JfCF7RaBQgBGKJBNFoFOFQCMKSEpSVlSGRSCDk86E8HIarpAR1Ph8nOy1RuAEhBOFwON35MRAIQCAQoKGhAWKxGJ2dnTg8PEQymcTQ0FBOzqNiOldTDrrFxUXMzMw8aTHD4/EgEokgEokAXGcGGQwG7O/vo6KiAhqNBjKZLG9OgpaWFlRVVWFubu67TQUoT0Mmk8Fms0GpVObkeM/dJeZwODgZqO/z+bC6uoqhoaFvxldaWgqhUIhIJMLpgHaWZeFwOGAwGOD3+9HY2AiNRnPv50wkEoHD4UBfX9+dP8fj8dDV1YWGhgZ8+vQJo6OjnBU5v4bH46GlpQVNTU04OzvD+/fv0dHRgaampqJ6ThYLPB4PVVVVCAaDnG+OQ6HkAh4phpqPIkRv0uN3/+R3UVtWi7ryh4cVJxIJ+Hy+a1HsFwubBJPAhfcCf2fq7+B3R38300OmFCuhEPBrvwZ4vcA9FzexeByxWAw1NTXw+3wQCIWo/sWiMx6PIxgKAQBqwmEIysth/xf/AqZYDH6/H5WVlVAqlZDJZAWxQ0rJDgzDwOPxpAWwZDKJysrKtAvpS/cXIQRra2uoqqpCb29vzsY4NzeH169f5+x4ucBoNOLq6gpTU1NZWciEQiEYDAZYrVaUlZVBrVZDoVDkJX8mFApheXn51sU65Wn4/X4cHR1hbGwsZ8eMxWJYWlrCmzdvnt0ifHFxEaOjo/fKAcwVBoMBp6en0Gq13xW8zGYzfD5fTu/b94FhGFitVhgMBkQiEUil0nTe10PR6/Vob29HY2PjvV8TjUah0+nQ3NyMlgJ00KdKRM1mM7q7u6FUKp/dNcl1TCYTgsEgurtphA6FQh1iHGVcOY6/2P4X8ccHf4zq0uoHd4UsKSlBXW0d3G53WhQz+AzoauzCb/b9ZpZGTSlKqqquBbF/9s8Alr0Owf8BpSUlCAYC4AGorauDz+tFKBxGVWUlSktL0VBaimQyiYTRCONPP6Gkvh4jKhV4PB5CoRAsFgv0ej2SySTEYjHkcjlnsogo2eFL95ff77/ORfyF+6u9vf27pW0Mw0Cn00GhUKC1tTXHo8a9m58UCmq1GolEAisrKxgfH8/471ZVVYWenh709PQgEonAaDRiYWEBQqEQKpUKKpUqZ2WMVVVVeP36NZaXl6HRaApy4clVamtr4ff7c3p9PGeXWDwe54wYRgjB9vY2EokEXr9+fafbVC6X4+DgAD09PXm/jyYSCZjNZhiNRiSTScjlcgwODj7JQerxeMCy7IPEMOC6E+CrV6+wvb2NtbU1vHz5sqBKEAUCAXp6etDe3o7Dw0McHx+jt7e3IJoGPBdkMhlOT0+pIEahgDrEOI01aMXv/JvfgSlgQmt966MmC/FYHIFgAInSBFjC4g/+8h/gdXNxORooOeDyEvjt3wYikXu7xFxuN0QiEfg8HggAr8eDsvJyVKZ2iu12gBAk/sW/wElZGcxmM5qamtDa2pp2i7AsC5fLBYvFArfbjZKSEsjlcigUCs63KKd8H5Zlb7i/EokEKioq0u6v+5bOJhIJLC0toaOjI2elWV8yPz+PqampogwRPj4+RiAQwMuXL3OyUI3FYjAajTCbzQCuOxWq1eqcLPJZlsXGxgZKSkowODiY94V5sbC+vo62tracln09R5dYJBLB9vY2tFptvoeCeDwOnU4HlUqFtra2e71mY2MDzc3NaGhoyPLoviUajcJoNMJisQC4vu+oVKobnSAfCyEEc3NzmJiYeFJJqNFoxMnJCSYmJgp23hOLxbC/vw+/34/+/v4HC4SU7PDp0yeapUmhgApinEdn1OHv/ru/C3fUjea65gc7xQghMHqMCEQC+Pvv/j7+k5H/5NlMEikZ5l/+S+C/++8AufzaNfYDgsEgBEIhKn4xsSSEwOP1oqK8HBU8HmAwAH/rb13/g+tF6dXVFc7OziAWi9HV1fXNpDQWi8FqtcJisSASiaC+vh4KhQISiaQoRYliIRqNwul0wuFwwOfzgc/no76+Pi2APaY0NhqNYnFxES9evMhbudvCwgK0Wm3Rtpvf29sDwzAYHBzM6XHj8XjaqcGyLBQKBdRqddZzho6Pj+FwOKDVaun9JANYrVa43e4fZidlmu3tbUil0mfjEjMYDIjH42hvb8/rOLxeL9bW1jA8PPwgwcPr9eLk5CRn5bVflm2XlpZCpVJBqVRmXBQwGo3wer03Ggk8Fr/fj9XVVfT39xe0yyoSiWB3dxfRaBQDAwMFk5FWrBwfH6O8vBwajSbfQ6FQ8goVxAoAnVGH3//z34fBb4CiWoGaspp7vS7OxHHlu0J1aTX+4/b/GNoyLaampgrKdk3hEIkE8Lf/NvCznwEaDfCDxWkimUQoFEL9F5kbhBB47XZUu1woefXqugzzqx1PQgjsdjuOjo5QWlqKnp6eW3M7CCHw+XywWCxwOBwAAKlUCoVCQcP58wjLsvB6vWn3VzweR3l5eVr8qqure/J3EwwGodPpMDY29qhMl0yxtLSE0dHRos6629raQllZGXp6evJy/GQyCYvFAqPRiFgsBrlcDo1Gk7UgfKvViv39fUxOTnI66LsQYFkWc3NzePv2bU6P+9xcYuvr62hvb8/rvfDq6grn5+fQarWPcle9f/8eb968ycr8lBCSbuzhcDhQWVkJtVoNuVyeNeGbYRh8+PABb968ydiGSTKZxMrKCurr6zlRYvoUgsEgdnZ2AAD9/f2oqbnfuoaSWcLhMD5//swJdymFkk+oIFYgmPwm/OOlf4yfn/0cCTaBhooG1JbVQsi/+aBlCYtQPARH2AFCCPol/fh7s38PY8oxXF5ewmq1YmJioqAfpJQ84vMBv/d7wMePgFgM3FHiQAC4XC6Iv9wp9vlArFbYm5vB+4M/gPQHO6c+nw+Hh4eIxWLo7OyETCb77rmbTCZht9thsVjS4fwKhQJyubyoBYt8E4vF0uKX1+sFgBvur0yXvKVcCFNTU3kvH9HpdBgaGspIeQ1XIYRgfX0d9fX1eXegMAwDm80Gg8GAcDgMqVQKjUaD2trajB7H7/djZWUFIyMj6U6ZlMexsLCAsbGxnOdbPSeXWEp4yce8jhCCra0tsCyL4eHhRwtah4eHqKyszJhThRACl8sFo9EIt9uNuro6aDQaiMXinGwKpzrqZjqXkBCCo6MjOJ1OTExMFHypm8/nw87ODkpLS9Hf35/3Z/pz5P3793j79i1dF1KeNVQQKyBYwmL+ah5/dPBHWLhaQCAeAAFB6itM3cwqhZXoEffgN/p+A3+p/S+hqvSXO+mnp6fwer0YGRmhNz/K4wgEgD/4A+Df/BsgHgdksu+WUHo8HtTW1kIQjwNW63Ug/6/+KpJ/5+9gYX8ffX19kEgkPzxkNBpNTwJbW1vR3Nz8w0ltKpzfZrMhkUjQcP4MkHLlpcofY7EYysrKbri/srnYsNvt2N3dxfT0NCcCpFdWVp7FJJ4QAr1eD7lcjubm5nwPB8C1+8jhcODq6gqBQABisRgajQb19fUZub5TLqPOzk6oVKoMjPh5cnZ2Bj6fn/OGBc/FJcYwDBYXF/Hq1aucHzsWi0Gn06GpqenJ328sFsPKygpmZ2cf/R6pe4LBYIDf70djYyPUajUaGhpyeg5Eo1EsLS1lVWRwOBzY3t7Ou0s6U7jdbuzu7qK6uhp9fX2ceL4/F7a3t6FSqWiuG+VZQwWxAsUcMOPQeYgT9wm8US9YwqKypBLtDe3oaOhAZ0Pndx/Eh4eHiEajGBoayvGoKUUDIcDcHPBP/ylweAhEo9elj5WVQMqNlUgg5vWCF4mgtLISaG8H/ubfBP7yXwZ4PCSTSczPz2NwcPDeD2KGYXB+fo7Ly0soFAp0dHTcy/1Fw/kfRzweT7u/PB4PAKCuri4tgOXSGWU0GnF+fo6pqSnOZHatrq6it7c3a+V7XIJlWSwtLaGlpYVzAhEhBE6nEwaDAV6vFyKRCBqN5sniN8MwWF1dRV1dHbq7u4taWMkW0WgU6+vrmJ6ezvmxn4NLzOFwwG63o7+/P6fH9Xg8WF9fz6iLcn5+HmNjYw96rjAMky6pjkQiWXONPoSVlRW0trZmPdsyGo1Cp9OhpaWFMxsVT8Vut2Nvbw+NjY3o6ekpeAdcIeB0OmE2m/HixYt8D4VCyRtUEHum7OzsgMfj5XwSRSkyGAbQ669zxTY3AZPpOmsMAEpKwIjFMEul0PyNvwFMTwNfTW7i8Tjm5+fx8uXLB02qCSEwm804OTlBdXU1enp6HiRKpML5rVYrwuEw6urqoFAoIJVKn22YdipnJSWARSIRlJaWorGxEWKx+LpjaJ7yB09PT+FwODAxMcGpDMT19XV0dnY+m/wThmGwsLCA7u5uzgY7E0Lg8XhgMBjgcrnSpVISieRRghYhBPv7+wgGgxgbG+PU+VcofPjwAa9fv875Z/ccXGIp8SCX1+PFxQWurq6g1Woz6uQxGo0IhULo7u6+8+dSTTdMJhOSySTkcjnUajUnNia8Xi/29/cxNTWVk+OxLJsuWX358mVR3J8IIbBYLDg4OIBCoUBXV9eznZflAkIIPnz4gHfv3uV7KBRK3qCC2DMllftQUVHxw8kHhXJvvN7rnDFCgNpaQCTCh48f71yQxGIxLCwsYHR09FHWf5fLhaOjI7Asi+7u7gfvyj7XcP5EIgGXywWHwwGPxwNCCGpra9PuLy4EihNCsLe3h1gshpcvX3Luu9jc3ERra2tRlKzcl5Szc2BgIG/dPR+Cz+dLh2lXVVVBo9FAJpM9eOFoMBhwdnaGyclJWs7zQHZ2diCVSiGVSnN+7K2tLchksqJ1iX369ClnrlmWZbG5uQkej4ehoaGMiy8sy+Ljx4+3Lsyj0SiMRiPMZjN4PB6USiVUKhWn8hsJIfj06RNGR0dzLs4ZDAacnp5Cq9Vy4tmdCQghMBgMOD4+RlNTE9rb24tC8OMiy8vLePHiBa2YoDxbqCD2jCGEYG1tDSKRKO9hyZTiZWNjA62trXe2145Go1hYWMDExMSj3TahUAiHh4fw+/3o6OiASqV6lIBSjOH8hBAEAoG0+yscDqOkpOSG+4trO7CEEGxsbKC8vBy9vb2cE8OA68V2c3Pzs2sdn3J2joyMFNTvHggEYDQaYbPZUF5eDrVaDYVCce9z3+12Y2NjA+Pj43ktySo0PB4PLi8vMTw8nPNjF7NLjBCCjx8/5qSLZ67K89bW1tDe3o76+nqEQiEYDAZYrVaUlpamr1eultGZzWY4nc68lZ6lmoEMDg7mRXzOFizL4uLiAufn52hra0NLS0vRXcv5xmAwIBaLoaOjI99DoVDyAhXEnjmEEOh0OiiVSjQ1NeV7OJQixGq1wuPxoLe3986fC4fDWFpawuTk5JN2V+PxOE5PT2E2m9HU1ITW1tYn7Z5/Hc7f2NgIhULB6XD+ZDIJl8sFp9MJt9sNhmFQU1MDiUQCsVjM+V1AlmWh0+kglUo5LdZ//vwZKpUKDXd0Wy1WUs7O8fHxgiwZDYfDMBqNsFgsEAqFUKvVUCqVP1xsh8NhLC8vo7+/n7Nlo1yDEIL379/jp59+ysvxi9UlFggEcHR0hNHR0awex+VyYXNzE6Ojo1kVwAkhuLy8xM7ODqqqqtJdJ2UyGec2bL6GZVm8f/8eb968yWvGZTKZxMrKCurr69HT08PZOcpjYBgGp6enMBqN6OzshFqtLqrfL58kEgksLy/npTkHhcIFqCBG4XRYMqXwSeUOvX79+oc/GwqFsLy8jKmpqSeLNizL4urqCufn52hsbERnZ+eTSwm4GM5PCEEwGEy7v0KhEIRCYdr91dDQwPnFxJckk0ksLi6ira0NarU638O5k93dXchksoIoHcwGkUgEi4uLTxax8000GoXJZILJZAKPx4NKpYJarf6uIzSZTEKn00Eul3NasOUSer0efX19qK6uzvmxi9UldnZ2BoFAkFXH1tnZGUwmE7RabVYc0oQQuFwuGI1GuN1u1NXVwWw241d/9VcL6rl1eHiIkpIStLW15XsoIISku3JPTExw1lH3WJLJJI6OjmCz2dDb2wuZTFZU13W+mJubw8zMTEFddxRKpqCCGAXA9UJ/YWEBXV1ddNebknEe8qANBALQ6/WYmZnJSD4IIQR2ux1HR0coLS1FT09PxjKfYrEYbDYbLBZLzsL5k8kk3G43nE4nXC4XGIZBdXV1OvurqqqqYCeHsVgMi4uL6O/vL4iSj729PUgkEkgkknwPJW8Eg0HodLqMXa/5Jh6Pp8UxlmWhVCqhVqu/+d0IIdje3gbLshgeHi7Yay5XmEwmhEIhdHV15eX4xegS0+v1GBgYyMpmDMuyWF9fR0lJCV68eJHR85tlWdjtdhgMBgQCATQ2NkKj0UAkEoHH42F/fx+1tbUFs0Gbcsu+e/eOU/cBh8OB7e1tjI2NFWXOZTwex8HBATweD/r6+p71czgTHB4eoqamBkqlMt9DoVByDhXEKGkYhsH8/Dz6+/ufreOBkh0e2gnL5/NhbW0NMzMzGQ2w9vl8ODw8RCwWQ2dnZ0Z3FrMRzk8IQTgcTru/AoEABALBDfdXPsszMknKHZjtspxMcnBwAJFI9Ow3EVLX6+zsbMHm7N1GMpmE2WyG0WhEIpGAXC6HRqO5IUCcnZ3BbDZjcnKyaK7FbJByft7HKZwNitEl9uHDh6zkh0UiEeh0OrS3t0Oj0WTkPRmGgcVigdFoRDQahVQqhVqtvjWLLxKJYH19HTMzMxk5drZZW1uDRqPh5CZOJBKBXq/PevZbPolGo9jb20MoFMLAwMCDOpZTfkkgEMDBwQHGx8fzPRQKJedQQYxyg0Qigfn5eQwPD9OHCiVjuN1uXF1dPShU2ePxYGNjIyuL7Gg0iuPjYzgcDrS2tqK5uTnj3YtS4fxWqxU+n+9e4fwMw9xwfyWTSVRVVaXdX9XV1UWzmPsSn8+H1dXVgiu9Ozo6Qk1NDRQKRb6Hknc8Hg82NzcxOztbdCU6wPW1abVaYTAY0gt6jUaDmpoa2O127OzsYHJykvP5fPlkbm4O09PTeRMOi8klFo/HsbKyknHRyOl0YmtrKyOuong8nhaUGYaBQqGAWq2+1zXy6dOnrJVpZhK/34/Pnz9zWrxjWRZbW1sghGB4eLhoOzWGQiHs7u4imUxiYGCANj55BO/fv8fbt2+Lcp5JodwFFcQo35Cyf4+OjhalzZqSex7bDcvlcuHz58+YnZ3NyiKKYRicn5/j8vISCoUCHR0dWZuAh0IhWK1WWK3WdDi/SCQCwzBwuVzw+/3g8/loaGiAWCxGY2NjUQoLX+NwOLCzs4OpqamCK7k7OTlBZWUlLTH4BQ6HA3t7e5idnS3qHJIvS76CwWDarXlwcIDh4WE0Njbme4ic5Pj4GBUVFXnLBiwml1iqC3J3d3fG3vPk5ARWqxUTExOPfg5GIhGYTCaYzeZ0Hp9KpXqw0/vq6irt5OYynz59wsuXL/OSjfdQrq6ucHZ2Bq1W++Q8VS4TCASws7MDPp+PgYGBgtpkyzf36QpPoRQjVBCj3Eo0GsXCwgK0Wm1BPOgp3GdhYQFjY2MPnhinFtkzMzNZcxYQQmA2m3FycoLq6mp0d3dn/LxnWTbt/nI6nfD5fIjH4wCAuro6NDU1QalUPiuHiclkwunpKaampgpS/Ds9PUVZWRnnw/9zidVqxcnJCaanp4vWifAlhBA4nU4YDAa4XC643W709fVhYGCg4EWXTBMKhbCzswOtVpu3MRSLS2x7exsqlSoj4ivDMFhbW0NFRcWjzttgMAiDwQCbzYbS0tJ0x9anPK8ZhsGnT5+yUhKaKVIbXA9xvucbv9+PlZUVDA4OcrLEM5N4PB7s7u6ioqIC/f39Bbfhlg/sdjscDgf6+/vzPRQKJadQQYzyXcLhMJaWljLS8Y9COT09RUlJCZqamh78WpvNhsPDw5x0wHG5XDg6OgLLsuju7n50nl4kErkhfvH5fIhEorT768sd+HyE8+ebs7Mz2Gw2aLXaghVOzs/PIRAIHnVOFzMGgwFGoxGTk5PPShRKiWPz8/OIx+Po6OiARqOBRCJ5Vp/DXeS7JCcWi2F5eRmvX78u6O9kbm4Os7OzT753RiIRLC8vo6ur694h9qm8TKPRCIfDgaqqKqjVashksow+r/R6PXp6ejhZ+sayLD58+IBXr14V3GZOMpmEXq9HQ0MDuru7C/o6uA9OpxO7u7uor69Hb28v58tw8wnLspibm+O0EE2hZAMqiFHuJNMd/yjPl6e6A8xmM87OznLmPAmFQjg6OoLP50N7ezvUavV3J44sy8Lr9cLhcMDlciEej6O8vBxisRgSieRBofqEEPj9flgsFtjtdgBPD+fnGvv7+wiHwxgZGSno3+fy8hIAijas+Cmcn5/D4XBgfHy8oL/jx0AIweHhISwWCxoaGuB2u1FdXZ0O3i5UATgTbG5uoqmpCQ0NDXkbw9bWFuRyecE2w2BZFp8+fcKbN2+e9D6pLoTj4+M/FJ0IIXC5XDAYDPB4PKivr4darc6q2Ot0OmEymTA0NJSV938KJycnAICOjo48j+RxpO5Rbrcb4+PjBSfqPRRCCGw2G/b39yGVStHd3U2boHyHxcVFjIyM0DUf5VlBBTHKDynWDmKU3PNUd4DRaMTV1RUmJydztqiMx+M4PT2F2WyGRqNBW1sbksnkDfcXgBvur0x2xnxMOD9XIYRgc3MTJSUl6O/vL3ih5OrqCgzDoLW1Nd9D4STHx8cIBoMYHh4u+O/6MZjNZhwdHWFychKJRAIGgwF2ux3l5eXQaDSQy+VF6wD9Hg6HA1arFYODg3kbQ6G7xDweDy4vLx9dqkcIwcnJCex2OyYmJr4rhnyZlRcIBCAWi6HRaFBfX5+Tz40Qgvfv3+Pdu3ec+p7i8Tjm5+c5N67HkBJFM9FEoRAghMBkMuHo6AgqlQodHR3P7h78Iy4uLkAIofMayrOCCmKUe+F2u7G9vZ21cHPK82B1dRXd3d2oqal59HtcXl7CYrFAq9XmZDLKsix8Ph/sdnt6EVFbW4uenh4olcqcLQ5ShMNhWCyWG+H8CoUCjY2NnJ6csywLvV4PsVhcsLvqX2M0GhGLxdDe3p7voXCWvb09sCyLgYGBfA8lL3i9XqytrWF0dDQdVBwKhWA0GmG1WlFSUpKRzKVC4bENVjJNIbvEjo+PUVlZee8Sxy9hGAarq6uorq5GX1/fN8+MZDKZ7qYai8UglUqhVqvzVra4u7uLxsZGTmW+bWxspDelioFIJAKdToe2trZnU/5PCMHl5SVOT0/R0tKC1tbWZ+3c/ZJYLIbV1VVOd06lUDINFcQo9+a5dBCjZA+j0YhIJPLkzlFnZ2dwuVwYGxvLuAgUi8XgcrngcDjg9XoBXIfei8ViiMVilJWVweFw4PDwEKWlpeju7s5bR55UUL/FYoHL5UJJSQnkcjkUCgWncv+SySSWlpbQ0tICjUaT7+FkDJPJhEgkUjQCX7bY2tpCWVkZenp68j2UvBCNRtM5TV93JI1GozAajTCbzeDz+emufIXm/nwIS0tLGB4ezmunu0J2iS0tLWFkZOTBTuRwOAydTofu7u4b52E8HofJZILJZALDMFAoFFCr1Zx4hoTDYWxtbWFqairfQwFwHSOytbWF2dnZfA8lo7Asi62tLRBCMDw8/GzEIZZlcXZ2hsvLS3R2dkKj0RTc/SAbfPz4Ea9evXo25wGFQgUxyoOwWq04Pj7GzMwMvVFSHkw8Hoder8/IZPL4+BiBQAAvX7589AQmFQ6cKn+MRqMoLS1Ni1/19fV3nud+vx8HBweIRqPo6uqCTCbL62SKi+H8sVgMS0tL6O3tLUg3xl1YLBYEAgF0dXXleyichhCCtbU1iESiZ+umYxgGKysrEIlE6OrquvU+EYvFYDKZYDabwbIslEol1Gp10WW5XF5eIplM5v1cKFSX2IcPHx7ssLPZbNjd3cX4+DhqamoQiURgNBphsVjA5/OhVCqhUqkyWu6fKebm5jjTiXhhYQGDg4OcDPrPBFdXVzg7O4NWq82rYJ1rGIbB0dERLBYLenp6oFAonrUwtre3h8bGxoK7N1Ioj4UKYpQHk8pxmpqaetYPDMrj+PDhA16/fp0RQTUlRg0NDd3rXIzH43C5XHA6nfB4PCCEoLa2Ni2APXYCGI1GcXx8DIfDgdbWVjQ3N+ddMOZCOH84HMby8jJevnwJkUiU9ePlGpvNBo/H82ydTw+BEAKdTgelUvlsynK+hhCC3d1dRCIRjI6O3nmPSCQSMJvNMBqNSCaTkMvl0Gg0nHDtPJVMbow8hZRY/+bNm4KZy0QiEWxtbWFycvJeP08IwdHREVwuF3p7e2G1WmGz2VBWVga1Wg2FQsH5Ut3z83OwLJt3AdVut8NoNGJkZCSv48g2Pp8Pq6urePHiBSQSSb6Hk1MSiQQODw/hdDrR19cHqVSa7yHlBZ/Ph9PT06I/1ymUFFQQozyKy8tLWK1WTExMFMxEksINPn/+DIVCAbFYnJH3293dBSHkm4wiQggCgQAcDgecTicikQhKSkrQ2NgIiUQCkUiUcdGKYRicn5/j6uoKcrkcHR0dnCl9YhgGdrsdFoslJ+H8qUm1VqtFdXV1xt+fC6TOrd7e3nwPpSBgWRZLS0tobW39pnTwOXF1dYWLiwtMTk7e69pjGAYWiwVGoxHRaBQymQwajaagr6uPHz9yIn6h0FxiBoMB8Xj8XuJQIpHA/Pw84vE4SkpKUF1dDbVaDblcnvcNm4eQTCaxsLDw5K6aTyEV8P9cmkslEgmsrKygoaEB3d3dz26eH4vFsLe3h0AggP7+fjQ2NuZ7SDmFEIIPHz7g3bt3+R4KhZITqCBGeTSnp6fwer0YGRl5dg9LyuNxOByw2WwZDdne2tqCQCCAWCyG0+mE2+0Gy7I33F+5dFYQQmA2m3FycoLq6mp0d3dzbvH6vXD+hoaGJy+WnE4ntre3MT09XXTlXl/idDphs9nQ39+f76EUDAzDYGFhAT09Pc929x0AXC4XNjc3MTEx8aAmIyzLwmazwWAwIBQKQSKRQKPRFFyHuP39fYhEorwHkxeaS2xjYwNtbW3f/b4JIXA6nTg9PcXe3h7a2trQ398PsVhcEL/f99DpdOjv78/bc/Ts7AzJZPJZlccTQnB4eAi3242JiQnOOwmzQSQSwc7ODuLxOAYGBgruPvsUVldX0dXVVbTlwRTKl1BBjPIkDg4OEIvFMDQ0lO+hUAoElmXx6dOnJ+32EkIQDAbT2V/BYBAOhwONjY148eIFGhoa8u48SOF2u3F4eAiWZdHd3Z0xZ1wmyWQ4v9lsxvHxMaanpzmR+ZJN3G43TCYTBgcH8z2UgiKZTGJ+fh6Dg4PPbuf9S1IlxQMDA48SBwkhcDgcMBgM8Pl8aGxshEajgUgk4rz44fP5cHJygtHR0XwPpaBcYh8+fPhGvGNZFna7HQaDAcFgEEKhEH6/H2/evOHcRsxjsdvtsNlsebnXJhIJzM3N4d27dwXlrMsUdrsdnz9/xvj4+LMVR4LBIHZ2dgAAAwMDRXNd3UWqmoBGQlCeA1QQozyZnZ0d8Pl89PX15XsolALh06dPmJycvLdgkkwm09lfbrcbDMOgpqbmG/fX6uoqGhoa8p41chuhUAhHR0fw+Xxob2+HWq3m7KL1seH8FxcXMJlMmJyc5IwgmU28Xi8uLy/phsAjiMfjmJ+fx8jISN66tHKBZDKJ5eVlKJVKtLW1Pfp9CCFwuVwwGo1wu92or6+HRqPhrDMoVZLz9u3bvI+vUFxiKXfl69evkUwm02W0sVgMUqkUarUaJpMJPp8PY2NjReXoSZUsvnv3Luff0dbWFiQSybMu845EItDpdGhra3u2GZDAtZC/s7OD0tJSDAwMFHXjAYZhMD8/n9dSZQolV1BBjPJkCCHY2tpCZWXls7KTUx7P0dERqqqqoFKpvvk7QghCodAN95dQKERDQwPEYjEaGhq+O9EnhECv10Mmk6GlpSXLv8XjSCQSOD09hclkgkajQVtbG6cXLvcN5z84OEAgEMDY2BinF5WZxOfz4fz8HMPDw/keSkESi8UwPz//4LLBYoMQgs3NTfB4vHs3CPnR+3m9XhgMBrhcLlRXV6OpqQkSiYRTDpe1tTV0dnZywnVSCC4xs9mMw8NDlJSUgGVZKBQKqFQqVFZWIplMQq/Xo7Gx8btdTAudz58/QyaT5bTUOhQKYW1tDa9fv87ZMbkKy7I37lNcupfkGpfLhd3dXdTU1KCvr4+T3VkzQer5/Bxy8yjPGyqIUTICIQSrq6tobGx80i435Xng9/txfHyM0dFRMAwDt9sNp9MJl8uFZDKJqqqqtPururr6QZN7QgiWl5ehVquh0Wiy+Fs8DZZlYTAYcHZ2hoaGBnR1dRXEbuPX4fwVFRUIhUIQiUR4+fJlUS7EvkcgEMDx8THtxPQEwuEwlpaWMDU1VRQdFJ/C6ekprFYrtFptRkVyv98Pg8EAh8OBiooKaDQayGSyvLs4LRYLvF4vJ5pScNUlFolEYDQaYbFY4HQ60dLSgv7+/hsL8EAggJWVFfT393Na0HsqwWAQu7u70Gq1OTvm0tIS+vr6nlV21I+4urrC+fk5JiYmCmLOkk3sdjv29vbQ2NiInp6eoouJOD09hVAoRHNzc76HQqFkFSqIUTIGIQQ6nQ5KpfJZW6op34cQgnA4DIfDgcXFRSiVSggEgrT7q7GxMSMLwVQ3u5aWlltdaFwilQN0dHSEkpISdHd3F0wJGcuyWFxcBMuyEAgESCQSaGhogEKhQGNjY9HvIIdCIRwcHHAiB6mQCQaD0Ol0mJmZKeomDPfBZrNhd3cXk5OTWREIQ6EQDAYDrFYrysrKoFaroVAo8uJS5VpJDldcYoFAAEajETab7cZ3tLy8jMnJyRvfVco1ptVqn4Wg/PHjR8zMzOTkfHU4HLi4uMD4+HjWj1VopLpIv3jxAhKJJN/DySuEEFgsFhwcHEChUKCrqyvvmw2ZIhKJYHNzE1NTU/keCoWSVaggRskohSREULIPwzDweDxp91cikUBlZSXEYjEMBgPGxsZQVVWVtWMvLi6is7Mz753M7ovf78fh4SEikQi6urogk8k45Vb4EoZhsLS0hKamprQAnsoxykQ4fyEQDoexu7tLF0wZwOfzYW1tDbOzs8++PCMQCECv1+Ply5doaGjI2nG+dB/x+Xyo1WqoVKqcuhy4VJKTL5fYlyWuTqcT1dXVaRdfalOBEIKPHz/i7du36f/f29tDMBjE2NhY0SzAf8Tp6Sn4fD5aW1uzepxUxt309HTRlsM9lUQiAb1eD7FYXLRlug+BEAKDwYDj42M0Nzejra2tKDYFb2vkQaEUG1QQo2SclBCRWtBTng+RSAROpxMOhwN+vx98Pv+G++vLhdbl5SUYhslqiW3KgdDb25vT3JGnEo1GcXJyArvdjpaWFrS0tHBqYhWPx7G4uIje3t47r/F4PA6r1XojnD/lwCiGBVw0GsXW1lZOS3iKGbfbje3tbczOznI6Vy8XxGIxLC8vo62tLSel37FYDCaTCSaTCQCgVCqhVquzLgZwrSQnVy4xQgicTieMRiM8Hg9EIhHUavV3myAEAgEcHh5ibGwsLURIpVJ0dHQ8q4VqIpHA0tJS1jO9Li4uEI1GaYe9H0AIwcHBAbxeL8bHx5/9fRu4NgZcXFzg/Pwc7e3taG5uLuhrdGdnBzKZ7Nk7ASnFDRXEKFkhmUxiYWEB/f39EIvF+R4OJQuwLJt2fzmdTiQSCVRUVKSzv74MXL+NaDSKjY2NrFuxk8kk5ufnMTAwUHDnIsMwOD8/x9XVFWQyGTo7O/PupIhEIlhaWsLw8PCD3Cu3hfOnOnf96FzhKvF4HOvr65icnMz3UIoGh8OB/f19zMzMFIVo+hRYlsXq6iqqq6vR29ubs2skkUjAbDbDaDQimUymxbFs5AVxrSQnJUS+fv064583y7Kw2WwwGAwIhUJobGyERqNBfX39D491fn4OPp8PkUiElZWVZ12qtrS0hKGhoay5jpPJJD5+/Ih3795xaiOKy9jtduzs7GBsbIwTTTK4AMMwODk5gclkQldXF1QqVUHOc9xuN66urmjzIEpRQwUxStZIJBKYn5/H8PAwRCJRvodDeSLRaDQtfvl8PvB4PNTX16cFsMcINbmyYhf6uUgIgdlsxsnJCaqrq9Hd3Y3q6uqcj8Pv92NlZSUjXQG/DuevrKyEQqGATCYrmBKVRCKBlZUVTE9P53soRYXFYsHp6Smmp6ef/YKUEILDw0P4fL68lMYlk0lYLBYYjUbEYjHI5XJoNJqMlrpzrSQnky6xrz8/mUwGtVr94PunXq9HQ0MDDAbDs8kL+x5WqxUulwv9/f1Zef+dnR3U19dDrVZn5f2LlUgkAp1Oh/b2dk43NMo1yWQSh4eHsNvtaVc9V+519yFVPvzu3bt8D4VCyRpUEKNklVgshoWFBbprVGCwLAufz5cWwGKxGMrLy9PiV11dXUYe6Jubm2hubs6JSJU6F0dHRwu6Y5Tb7cbh4SFYlkV3d3fOXG8ulwtbW1uYmprKilMkHA7DYrHAarUWTDh/KkdtdnY230MpOgwGA4xGIyYnJwtq8ZAtTCYTjo+PMTU1lTfBmGVZWK1WGAwGhMNhSKVSaDSaJz/bP3/+DLlczhnH01NdYrFYDGazGSaTCSzLQqFQPMlhRwjBH/7hH6KlpQWjo6PP3jlJCMH79+/x7t27jN8bwuEwVlZWsuIQfA6wLIuNjQ3w+XwMDQ1x9tmdD+LxOPb39+H1eguuekav16O/vz9rmb8USr6hghgl60SjUSwsLECr1ebF1UL5MbFYLC1+eb1eALjh/srWAsxqtcLj8aC3tzcr7/81qXMxEw6nfBMOh9POkfb2dqjV6qxN4K1WKw4PDzE1NZWTks3bwvllMhkUCgWnJmSEEHz69CnreTbPlfPzczidToyNjdHFKQCPx4P19XWMjY3lXdRnWRYOhwMGgwF+vx9isfje5X9f43K5YDQaMTQ0lKXRPpyHusS+blCgUqmgUqmefL+Mx+NYWlqCz+fDr/3arz3pvYqJzc3NdOZaJlleXkZ3d3dBOsm5xOXlJS4uLqDVap995+CviUaj2N3dRSQSwcDAQEF0FTcajQiHw+jq6sr3UCiUrEAFMUpOCIVCWF5exvT0dFbcJZT7Qwi54f6KRqMoKyu74f7K1a4ewzBYWFjIqaAQiUSwuLhYNAJtIpHA6ekpTCYTNBoN2traMhpse3l5CYPBgKmpqbw5E74Xzi+VSvMe4vvx40e8efMmr2MoZo6OjhAKhTA8PExFMVzfv5aXl9Hb28uZ7rkpAdtgMKQD4jUaDRobG+/1nXGxJOc+LrFAIACDwQC73Y6ysjKo1WooFIqM3ZN8Ph9WV1ehVCrB4/FowPsXpDoyZ7LDr8vlwunpKSYmJjL2ns+Z1Pn7nPPu7iIUCmFnZwcsy2JgYIDTm7TJZBKLi4t0849StFBBjJIzUq3kZ2Zm6I5RDonH4zfcX4QQ1NXVpQWwfH8Xc3NzOQ/QTgm0U1NTRZPFwrIsDAYDzs7O0NDQgK6urieLz0dHR/B6vRgbG+NM6cP3wvkVCkXGSnkfAhXEss/u7i4IIRgYGMj3UDgBwzDQ6XSQSCTo7OzM93BuQAiB1+vF1dUVXC4X6urqoNFoIJFI7rw2dTodBgYGOOUA/dollvrdDAYDXC4XqqurodFoIJVKM35/NBgMOD09hVarxcnJCZRKJRobGzN6jELnw4cPePXqVUbmDoQQfPz4EVqtlm7aZpBUR1SxWIyuri66qXELfr8fOzs7EAqFGBgY4OycdG5uDtPT03nfhKRQsgEVxCg5xev1Yn19HbOzs3nvlleMpMSClAAWiURQUlKSFr9EIhFnhI0U+/v7EIlEOXc7pATaYnMtEkLgcDhwdHQEoVCInp6eB1vyCSH4/PkzCCF48eIFpyext4Xzy+VyyOXynGQtzc3N0V3THLC5uYmKigp0d3fneyicIHWNJhIJvHz5knP39RQ+nw8GgwEOhwNVVVVQq9WQy+XfjNdgMCAajXJK4IvFYlhaWkJfXx+MRiO8Xi9EIlG6VC8b90VCCLa3t5FIJDAyMgI+n4+5uTnMzs5y9jvOF8fHxygrK0NTU9OT3+vq6grBYBB9fX0ZGBnlSwghODg4gNfrxfj4OBVUvoPH48HOzg6qqqrQ19eX983qrzk+PkZFRQVtNkEpSqggRsk5brcb29vbmJ2dpQ/GJ5JIJOByueB0OuF2u8Gy7A33VyEIPR6PB5eXl3lp6ez3+7G6uoqZmZmC6Wz4EFJlJZFIBJ2dnZDL5T9cxBFCsLKygrq6uoIUH3Idzk8FsdxACMHa2hoaGhrQ1taW7+FwhouLCxgMBkxOTqKkpCTfw7mTYDAIg8EAm82GsrIyaDQaKBQKCAQCJBIJLC8v49WrV/keZrp5gNFoxNnZGdRqNQYGBrLuQI3H49DpdFCpVOlznBCCubk56kK9hVgsBr1e/+RzhmEYfPjwAW/fvn32DQuyic1mw+7uLm2y9QMcDgf29vYgEonQ09PDGfNAqsRTq9XmeygUSsahghglL6Ru+LOzs3QCck8IIQgEAmn3VzgcRklJCRobG9Pur0L8LFOlCm/fvs3L8Z+DazEajeLk5AR2ux0tLS1obm6+9VxhGAbLy8tQq9Vobm7Ow0gzSy7C+akgljsIIdDpdFAqlRlxhRQLTqcT29vb0Gq1nCo5vItwOJwOoRcKhVCr1Tg7O8OrV6/yslGWTCZhsVhgNBoRi8Ugk8mgVqtRWlr6pI6T98Xr9WJtbQ3Dw8M3SiO9Xi8uLi7ysmFUCCwsLGBkZORJm397e3uorq6m95QcEA6Hodfr0d7eDo1Gk+/hcBZCCGw2G/b39yGTydDV1cUJA8H79+/x9u1bTlcNUCiPgQpilLxhtVpxcnKC6elpWgpwC8lk8ob7i2EY1NTUQCwWQyKRcDZn4DEsLi5idHQ0by4tt9uNra0tzM7Oct5l8RQYhsHFxQUuLy8hk8nQ0dGR/sxT3cy6urqgUCjyPNLs8HU4f21tLRQKxZPC+akglltYlsXS0hJaW1uhVCrzPRzOkMpFLMQA62g0CpPJhO3tbZSWlqK3txcqlSrrz4NYLAaTyQSz2QyWZaFQKKBWq78RVx7acfKhXF1d4fz8/NaOfLRM6W5MJhP8fv+jO1VHIhHodDq8efOGLvJzBMuy2NjYgEAgwIsXL+j8/w4IITAajTg+PoZarUZ7e3teN763trag0WjQ0NCQtzFQKNmACmKUvGI0GtPlHndORmIx4OwMsFoBhgFKS4GmJkCjAQrQFfU1hBAEg8G0+ysUCkEoFKbdXw0NDQXp/rovZ2dnEAgEeXUlpVyLMzMznNiJyyaEEFgsFhwfH6OqqgrNzc34/PkzhoaGnk1wc6bC+akglntS3Wl7enoglUrzPRzOkCo71Gg0aGlpyfdwHkwgEMDOzg7kcjlMJtOdItVj+dKZJhAIoFKpoFKp7nQH36fj5GNgWRZbW1sghGB4ePhWYWBpaQkvX77kXJ4QV2BZNt2h9DHfTcqt9Fyee1witTl3mxBMuQkhBBcXFzg7O0NraytaWlryIiQ6HA5YrVYMDg7m/NgUSjYp7lUfhfOo1Wokk0msrKxgfHz85oQmHgc+fQL++I+BjQ0gGASiUYCQaxGsogKoqwPevAH+6l8FhoeBAtnhSyaTcLvdcDqdcLlcYBgG1dXVEIvF6O3tRVVV1bParZTL5fj8+XNeBTGJRIKenh4sLi7mvOtlruHxeFAqlVAqlTAajfjTP/1TqFQqsCyb76HlDB6Ph7q6OtTV1aGnpwcMw8DhcODs7Cwv4fyU+yMQCDA9PY35+XkIBAK6mP0FJSUlmJmZwebmJvx+PwYHBwvqOVJTU4NoNIqWlha0trYimUzCbDZjY2MDiUQCMpkMGo3mwWWhfr8fRqMRdrs9nV32kI2PsrIy1NfXw263Z8wlFovFoNPp0NTUdKd4GYvFqFhwB3w+H/X19fB4PA92rXg8HrAsS+8feaKlpQX19fVYWFjA0NAQxGJxvofEWXg8HlpbW9Hc3IzT01N8+PABHR0d0Gg0Ob3Hi8Vi7O7u5ux4FEquoA4xCic4OTmB3+/Hy5cvr2/uCwvAP/7HwNERwLJAbS1QVQWUlwN8/rVLLBwGQiEgEAAqK4HJSeD3fg/gWOAyIQThcDjt/goEAhAIBGhoaIBEIkFDQ0PRO5LuA1eyCSwWC05PT59FKa/H48HGxgYmJycBAIeHh/D5fGhvb4darc77d5FP7hvOTx1i+SMej2N+fh4jIyMP7qRa7BwfH8PhcECr1RaUuL+xsYGWlhaIRKIbf84wTDroPhKJQCqVQqPRoKam5pv3IITA6/XCYDDA6XSipqYGGo0GUqn00ff0TLrEPB4P1tfXMTIy8s3v+SWRSARbW1vp+zPldrxeL05PTzE6Onrv16SaFYyPjxdV/EQhEo/HsbKyAolEgs7Ozmc977gvDMPg6OgIFosFPT09UCgUOfvclpaWMDw8XBBNuyiU+0IFMQpnODg4QCIUwuDPfw78638NRCKASnUtgt0FIdeimNUKyGTA3/7bwK//et7cYgzD3HB/JZNJVFVVpTs/VldX0wf+LaytraGzs5MT3YeMRiMuLy8xNTVVtKJYKrB1amrqhgMqkUjg9PQUJpMpnVnx3AXbVDi/1WqFy+WCQCCAXC6HQqHA2toaFcTySDQaxcLCAiYmJm4VR54zVqsV+/v7mJycLJjFi91uh8PhQH9//3d/hmVZ2O12GAwGBINBiMViqNVqxONxmEwmeL1eiEQiaDQaNDY2Zux5m4kssYuLC1xdXUGr1f7QeWowGBCLxdDR0fHo4z0X3r9/jzdv3tz7eW00GuH1ejEwMJDlkVHuAyEE+/v78Pv9GBsbe/ZzjvuSSCRwcHAAl8uFvr6+nEQIXF1dIZFIoL29PevHolByBRXEKNwhHofjP//PUfvnf44yuRxoaHiYqMWygMl0/d//9X8N/PW/np1xfsWX7i+/3w8+n4+GhgaIxWI0NjYWdUh7JjGZTAiFQujq6sr3UABcP/TNZjO0Wm3RCZgGgwEXFxeYmpr67sSTZVkYDAacnZ1BJBKhu7u7YBbV2ebLcP79/X0MDg4+OZyf8njC4TCWlpYwNTVF3R5f4ff7sbKy8kM3EldgWRZzc3P36jqcco0dHh7CarWCz+ejqakJXV1dGRXCUqRcYm/evHnwa1mWxebmJvh8/r2DxDc2NtDW1oa6urrHDPdZcXh4iKqqqns1H2BZNi2g0fs1t7DZbNjd3cX4+Djd4HgAsVgMe3t7CAaD6O/vz2rofTweh16vx+zsbNaOQaHkGiqIUbjDP/knIP/z/4xgVRV4dXWofmz7+FTw/n//3wN/8S9mdIgsy8Lj8cDhcMDlciGRSKCiogISiQRisRg1NTVFJ57kikQiAZ1Ox6mH7Pn5ORwOx7f5dgXM8fEx3G43xsfH77UoI4TA4XDg6OgIQqEQPT09tDztCz5+/Ijh4eEnh/NTnkYgEIBer8fMzAzNXPqKWCyGpaUldHZ2QqVS5Xs4P2RxcREjIyO3fo+pXDGj0Yh4PJ7OFauurgYhBB6PBwaDAS6XC3V1ddBoNJBIJHl1iUWjUeh0OrS2tqKpqener/vw4QPtfnhPotEoVldX7zV/ODg4QHl5eUE2nngOhMNh6PV6dHR00O6qDyQcDmN3dxfxeBwDAwNZE9M/fvyI2dnZgirHp1DuggpiFG6wvAz8F/8FUFoKIhbD5/WipLQUVY/d7T8/vy63/Jf/EniChTgSidxwf/F4PIhEorT7667OVJSH8/HjR7x69YpTZYonJyfw+XwYGRkp6IUJIQQ7OztgGAZDQ0OP+l38fj8ODw8RiUTQ2dkJuVxe0J9JJvg6QywVzm+xWOD1elFRUQGFQkHD+XOAz+fD2toaZmdn6b35KxiGwerqKurq6tDd3c3p6/b8/BwA0NraCuBa0DOZTDCZTCCEQKlUQqVS3elYJYTA5/PBaDTC4XCgqqoKTU1NT8oRS43lIS4xl8uFzc1NjI6OPmgjIdVJlZZj35/5+XmMj4/feZ+NRqNYWlriRF4p5fuwLIuNjQ0IBIJ7OyopvyQQCKTD7wcGBlBdXZ3R9z84OEBdXR0UCkVG35dCyRdUEKPkn0QC+K3fAnZ2gPZ2gMcDAeD1eFBeXv64Mq1kEjg7A37nd4Df//17vYRlWXi93rQAFo/HUV5ens7+om6P7PP582fI5XJIJJJ8D+UGKRHosUJSviGEYG1tDVVVVejt7X3y+8ViMRwfH8Nut6OlpQXNzc3PdqfwR6H64XA4XV55Vzg/JTO43W5sb29jdnaWlkN9RSqnJxgMYmxsjLPnX8rRplQqYbFYIBQKoVKpoFQqHy10BgIBGAwG2O12lJeXQ6PRQC6XP+q+dV+X2NnZGUwmE7Ra7YPH7XA4YLPZaMbVAzAYDAiHw+ju7v7uz6yurqK5uZlzcwzK7VxcXODy8hJarZY6fx+B1+vF7u4uysrK0N/fn7HYC7/fj6OjI4yNjWXk/SiUfEMFMUr+mZu7dofJZMAXN+tU+UNlZeXjHoR2OyAUAn/0R8Atk59oNJoWv3w+HwDccH9RN0fucTgcsFqtGBwczPdQvmFvbw8Mw3BybHfBMAx0Oh0UCkXacZHJ9764uMDFxQXkcjk6Ojqe3XXzkC6TX4bzO51OCIXCdDh/1WNLxCnfYLfbcXBwgJmZmWcr1N5FKhtwcnKSU9er3++H0WiE3W6H0WjE69evoVQqMy5shsNhGAwGWK1WlJSUQK1WP+g4P3KJsSyL9fV1lJSU4MWLF4/aRNnf34dIJIJcLn/wa58rLMvi48ePePfu3a1/7/P5sLe3h6mpqRyPjPIUvF4v1tbWMDQ0BLFYnO/hFCQulwu7u7uora1Fb29vRu77XOkMT6FkAiqIUfLP7/0e8G//LXBLJyVCCNxuN6qrqx9+A2eYa5fYf/vfgvzWb91wf8ViMZSVlaXdX/X19fSmzgEeEqicD7a3tyEUCtHX15fvodyLRCKBpaUldHR0QKlUZu04hBBYLBYcHx+jqqoK3d3dzyYQ9yGC2NfE43HYbDZYLBaEQiHU1tbScP4MYTabcXZ2hunpac46ofKJ2+3GxsYGxsfH89bZ9+vMr5qaGmg0GkilUhwcHKCxsfFJHR3vQzQahdFohNlsBp/Ph0qlgkql+qGja3NzEwqF4pvxRSIR6HQ6tLe3Q6PRPHpc8/PzmJycpPeBB7K6uoqOjo5by1Pn5uYwOjpKNx8KkFSQu0wmQ0dHB52vP5JUd3GxWIzu7u4nNf1aX19He3s7bfpBKQqoIEbJL8kk8Cu/AoRC1w6xW2AJgcftRk1Nzb3LDhiWRSIeB05PYZ+YwPl/9p+hrq4uLYBR6zV3mZ+fh1ar5WR3TkIINjc3UVlZeWdZBheIRqNYXFzEixcvcrqr6vF4cHh4iGQyie7u7qIvTZmbm8OrV6+ePEEnhCAQCKTD+QkhNJz/iRgMhnTJGv38viUcDmN5eRn9/f1ZF55SpJp0GAwG+Hw+NDQ0QK1Wf9MV0uv14uzsDCMjIzkZF3C96E5llbEsC6VSCbVafet84TaXmNPpxNbWFsbGxp60SCSE4OPHj5zdGOIyLpcLV1dXePny5Y0/N5vNcDgcGBoaytPIKE8lVfLt9/sxNjZGxeJHQgiB2WzG4eEhlEolOjs7H+WkttlscLlcBbNBTKHcBRXEKPnl7Az4zd8Eamqu//kOLMvC7Xajtq4OpV8JJQRAMpFAPB5HPB4Hw7Lg8/koLS1FmdsNoVwO3p/+KUAXRAXB0dERKisrOdtdKJXHVV9fj45bXI1cIBgMQqfTPXlh9hTC4TCOjo7g9XrR1tYGjUZTlKLEp0+fMDMzk3EXEg3nzwxnZ2dwuVwYGxsryvPvqSSTSeh0OsjlcrS3t2flGAzDwGazpTOeJBIJ1Gr1nUIvIQTv37/HTz/9lJUx/YhUN0uDwYBkMgm5XA6NRoPKLxr9fOkSOzk5gdVqxcTExJMbOgSDQRwcHNB8nkeQOm/evn2bviezLIv379/jzZs3VEQpAqxWK/b29jA+Pv5snOjZgBCCq6srnJycoLm5GW1tbQ+ax3C9ooNCeQhUEKPkl8VF4Hd/F1CrgR84ghiWhcftRm1tLViWRTweRyKRAAAIhUKUlpaitLT05k6H2w2wLPDv/t2dghuFOxRCWCchBCsrK5BIJBnP5XoqqbyNqampG4u3fJFIJHB2dgaj0Qi1Wo329vaiWpQsLCxAq9Vm/XdKhfNbrVbE43Eazv8ACr0pRrYhhGB7exssy2J4eDgjn1EikYDFYoHRaEQ8HodcLodarX5Qt7OVlRX09PTkfdHLMEz6d4lGo5DJZFCr1SgtLcXi4iKqqqpQWVmJ/v7+jHx25+fn4PF4aGlpefrgnyH7+/uoq6tLxwQcHR1BKBSira0tzyOjZIpwOAydTofOzk7Obp4WCizL4vz8HBcXF2hvb0dzc/O972MLCwsYGxujm3SUgocKYpT88ukT8Df/JtDSAtzDssswDILBIAQCAUpLS1FSUnL3jdvjuS7L/NM/BUSizI2bkjUIIfjw4cN3g3G5AiEEy8vLUKlUaGpqyvdwAFyHie/u7mJ6eppzExSWZWE0GnF6egqRSITu7u6MdTzKJ0tLSxgbG8tpiW8qW9FisdBw/nuSakHf39+f55Fwl/Pzc5hMpkdnV8VisXTJISHkzpLD+2A2m+H3+9HT0/Oo12cDlmXTbjev14uLiwsMDQ1hYmIiY2LrysoK+vr66LX8SCKRCDY2NjA9PY14PI75+Xm8e/eOiuFFBsMw2NjYeFLzCsovYRgGJycnMJlM6Orqgkql+uFnen5+DgCc2ximUB5K8WzTUwqT8vJrIYxh7iWICQSCh5WAMQzA518fh1IQ8Hg8VFZWIhwOc8Lh9D14PB60Wi2WlpYgEAigUqnyOh6j0Yjz83O8evWKkw4sPp+PpqYmaDQaOJ1OrK2tQSgUoqen59YA5EKBx+OBZdmcH7OxsRGNjY0AfhnOv7u7S8P5v0NfXx+2trZweHjI+fy/fNHa2oqqqirMzc1hcnLyXvffLzs2CoVCqFQqaLXaJ5cNAoBMJsPx8TGnBDE+nw+FQgGhUIjt7W28efMG6+vrCIVCaGxshEajgUgketLiPBQKcfrZx3UqKiqQTCYRj8exs7ODgYEBKpYUIQKBAGNjYzg/P8enT58wMTFB84GfgEAgQHd3N9rb23F4eIiTkxP09vbemS+pVCqxurpKBTFKwUNnypT80twMVFYC4TCQgQn0N0QiQFcXUAROlOeEXC6HxWLJWqZNpuDz+ZiamsLCwgIEAgHkcnlexnF6egqHw5GVLKtMw+PxIJFIIJFIEAgEcHBwgEgkgo6ODigUioJbuPD5/JwLYl9TWloKjUYDjUZzI5z/9PSUhvP/Ah6Ph6GhIayuruLs7IyWT30HqVSK8fFxLC0tYXh4OC26fonf74fBYIDD4UB5eTnUajVmZ2cfFcx8FwKBAHw+H4lEgjNNVgghOD4+hsPhwOvXr1FSUoJIJAK5XI6SkhJcXV1hc3MT9fX10Gg0EIvFD7rmEokEhELhs71OM0Vzc3P62SKVSvM9HEoWaW1thUgkwsLCwnfvWZT7IxQK0d/fj3g8jv39fRweHqKvr+/W5kxlZWVIJBJgf5HdTKEUKrRkkpJfCAF+7dcAo/E6RyzTHB8Df/2vA7//+5l/b0rWiMViWFtbw/T0dL6Hci8YhsHCwgJ6enpyOvkmhGBvbw+xWAwvX74s2EVULBbD8fEx7HY7mpub0dLSkvHFdbZYWVlBf38/Zx0dNJz/JoQQ6HQ6qFQqaDSafA+Hs8TjcSwvL6OlpQUajQYejwcGgwEulwu1tbXQaDSQSCRZXwSdnJygrKyME98VwzBYXV1FdXU1+vr60vfbrztOEkLg8/lwdXUFl8uF6upqaDQaSKXSH35eVqsVXq+XU664QoRhGPyrf/Wv8Ou//ut5z6Cj5IZ4PA69Xg+ZTIaOjo6CnQ9xjUgkgr29PUQiEQwMDHzj6N/d3YVYLM5Zp2IKJRtQhxglv/B4wLt3wP/0P12LY5l8gEWjgFAITExk7j0pOaGsrAzxeByEkIKY1AgEAkxPT2N+fh58Pv/WnbRMQwjB+vo6KioqCloMA66/74GBATAMg4uLC3z8+DE9qeW6aMMFh9hdpJyLKfdiKpx/bW3tWYbz83g8TExMYHFxEQKBIB28TblJSUkJurq6sLi4iKWlJXR3d0Oj0eQ8q0epVGJ7ezvvglgoFIJer0d3d/c350xZWRnq6upgs9kgk8nA4/FQX1+fXjimHHUHBweoqKiAWq2GXC6/VfR3OBxQKBS5+JWKGofDgerqatA9/+dDaWkpZmZmsLe3l+6yTSMDnk5FRQVGR0cRCoWws7MDlmUxMDCQFppVKhXOz8+fJogRAkTMQNgAsHGAJwDKZUBVC8Cn3yEl+1CHGCX/HB4Cv/3b1zlfmQy+v7q6dp39b/8bwPFFNeVbNjc30dzcDFEBNUNIJBKYn5/H0NAQGhoasnYclmWh0+kgk8mKsvSLEAKLxYKTkxNUVFRwotPc99jY2EB7eztqa2vzPZQH82U4v8vlSotnzyGcP1+uTi7DMAysViuMRiPC4TAkEgk0Gg0sFgs8Hg8mJiby4tx8//493r59mzfRP5XPNz4+/t370Ncuse8RCoVgMBhgs9lQWloKtVqdziQDgLm5OczOzj4LcTpbEELw/v179PT0wOFwYGhoKN9DouQYq9WKvb29O69ZyuPw+/3Y2dmBUCjEwMAAKioqHtcIixDAtweY/3fA9h6IuQAmDBAWAA8QlAMltUDDGKD+q4B4mopjlKxBBTFK/iEE+Ht/D/ijPwI6Ou4Vrv9DwmHAYgH+m/8G+K3fevr7UXKO1WqF2+1GX19fvofyIFJdrUZGRrISFp9MJrG4uIj29va8B/nnAo/Hg8PDQySTSXR3dz84kyfbbG5uorW19WHNPjhKKpzfYrE8i3D+ZDKJ+fl5DA4OPtvcmUQiAbPZDJPJhEQiAZlMBo1G840gajabcXR0hMnJyZwHV29vb0OlUuX8OyKE4PDwEG63GxMTEz+8BjY3N6FQKO7tlIhEIjAajbBYLODz+WmnxU8//ZSJ4T9bTk5OAADt7e14//497TD5TAmHw9DpdOmOiZTM4vF4sLOzg6qqKkSjUQwODt5ffAwZgIN/Atg/AckgIKwChNWAoOLaHUYIwEQBJgQkAtdCWG0P0Pt3AbE2u78Y5VlCBTEKN7BYgN/5net/t7Q8rXSSYYDTU+DNG+Cf/lOAI2G8lIeRcnC8fv0630N5MNFoFAsLCxgfH8+ocygWi2FxcREDAwOQSCQZe99CIBwO4+joCF6vF21tbVCr1ZxwUaTKuQrJyXgfvgznt9vtRRvOnxKwR0dHi0LUvA/RaBQmkwlmsxkAoFAooFarfyh0eb1erK2tYXR0NKedYZ1OJ8xmM168eJGzYyaTSaysrKC+vh49PT33Ot/v6xL73msPDw9xcHAAhUIBpVIJtVrN+ZJxrpG6nt++fQs+n4+dnR2IxeK8Nbyh5BeGYbC+vo7S0tKcl3s/FxwOB3Q6HYRCIX766acfdxg2/R/A/v9wXSJZJrt2gf3oe2Ei1z8vqARafhvo/lsAn67tKJmDCmIU7vCznwH/4B8AicR1qeNjHlwMA5yfA01NwP/4PwJFWE72nJibm8PMzEzBBKx/SSQSweLiIrRaLaqrq5/8fqFQCMvLyzlfjHKNRCKBs7MzGI1GqNVqtLW15bUD3c7OTjqDq5gp5nD+lICdqWuVi4RCIRiNRlitVgiFwnSp3g8XL18RjUaxvLyMrq6unOWvEUIeV5LzSILBIPR6Pfr6+h4spDzUJfYlJycnKC8vh0wmg9lshtFoRDKZhEKhgEajQQXtlv1DNjc3b2QmhkIhfP78GZOTk3keGSWfnJ+fw2AwQKvVFvzzioskk0n82Z/9GSorKyGTydDV1XW7o/byfwX2/p8AmwAqNQDvAZuahABxF5DwAprfAAb/b1QUo2QMKohRuMUf/zHwD/8hEAxei1oPWeiGQoDJdO0w+yf/BBgYyNowKblhf38fIpGoYHd3w+EwlpaWMDU19aQuhD6fD6urq5icnCz6bKf7wrIsjEYjTk9PIRKJ0NXVlZdOj3t7e5BKpTlppMAlIpEILBYLrFZrUYTzZ+pa5RI+nw9GoxF2ux0VFRXQaDTfDXN/CAzDQK/Xo6GhAV1dXTlxXSwvL+PFixdZ/25S2UNarfZR99qnuMSWlpbw8uXLG069ZDIJi8UCo9GIWCyWLmktVuH2KQSDQWxsbODVq1c3/vzjx4+Ynp7O68YJJf94PB6sr69jeHi46Dew8sGnT5+g1Wphs9lwfHwMtVqNjo6OX84HbO+B9d8DwAIVTyhhTfiBmB1o/12g97/KyNgpFCqIUbjH4iLwj/4RsL8P1NQAYvHdwlgkAtjtQDIJvH4N/P2/fy2KUQoej8eDi4sLvHz5Mt9DeTTBYBA6nQ7T09OP2uF3OBzY2dnB1NRUzrN7CgFCCJxOJw4PDyEUCtHd3Z3T8sX9/X00NjY+62D2YgnnDwQC0Ov1mJmZKchrLfU9GI1GuFwu1NXVQa1WQyKRZFykJIRgd3cXkUgEo6OjWRdBr66uEI/H0dHRkZX3J4Rgf38ffr//yd3pHusS+/DhA96+ffvdv2dZNt30IBQKQSqVQqPRFGRDj2ywsLCAwcHBbz6P8/NzEEKKsgEN5WHE43Ho9XrI5XK0t7fTEsoMcnJygtLSUjQ1NYEQgouLC5ydnaG1tRWtylrwlv8GELwAqlqfFosDADHntcts/J8B4omMjJ/yvKGCGIWb+HzA//K/XAft22zXVtny8ut/eDyAZa+D8+Pxa7Gsre26U+V/9B8BRRj+/FwhhODjx493LhIKAb/fj5WVlQcvtE0mE05PTzE1NUV3t+9BIBDA4eEhwuEwOjo6oFAosj7hPTw8RF1dXcG6GLPBbeH8crkcMpmM8+H8Pp8Pa2trmJ2dfXA5YT5gWRYOhwNGoxE+nw8NDQ3QaDRoaGjIyWLv6uoKFxcXmJyczOrnFY/HodPpvnH/ZIJkMgm9Xo/GxsaMON4e4xKLRqPY2NjA1NTUvX4+9b0bDAb4/X6IxWJoNBrU19c/y0W+3W6HwWDA6OjoN3+XTCaxsLDwKNcepfgghGBvbw/BYBBjY2MFGcnBRcLhMLa3t2+UJ7Msi9PTU/D2/xGamT+HsK4LvEx0iiQECJ0Bdf3AzP+Pdp+kPBkqiFG4jd8P/PznwPb29T9W6/WNkM+/FsFevABGR4HZWRqeX6TcVkZSiHi9Xqyvr997oX12dgabzQatVluQJWj5JBaL4eTkBDabDc3NzWhpacnapPf4+BhVVVU5y1MqNL4O52dZFhKJBEqlkrPh/C6XC58/f8bs7CwnBTyGYdJOoXA4DKlUCrVanbemAC6XC5ubm5iYmLh/l7FH8PHjR8zOzmb0Wg4EAlhZWcHAwEBGXZ4PdYkZjUZEo9FHOeAIIXC5XDAYDPB4PBCJRNBoNGhsbOTk9ZVpCCF4//49ZmZmvpsPtby8jIGBAVpqSkmTKo+emJig50WG+PDhA968eXPzvhNzg/34VxEN+RFmq1FdXY2ysnI8+c7ERICoDRj/p4C08JpvUbgFFcQohUUicV0aWVJCnWDPhLOzMwgEAjQ3N+d7KE/G7XZja2sLs7Ozdzq+9vf3EQ6HMTIy8iwWNNmCYRhcXFzg8vISUqkUnZ2dGQ/UPTk5QUVFBW3rfk8KJZzfbrfj4OCAM009EonEjaB1uVwOtVrNmZLUcDicFh2yVT58cHCA2trajInPZrMZh4eH0Gq1Gc8mi0aj0Ol093YlbWxsoLW19ckNUwgh8Hq9MBgMcDqdqK2thUajgVQqLdpnydnZGRKJBLq7u7/7MzabDQ6HAwM0W5byBaFQCHq9Hl1dXfQZngG2t7ehVCpvZqpe/mvg8/8dqGwBy+MjGAwikUiguroGZU91FQeOAdVfBUb+h6e9D+XZQwUxCoXCaW6zYRcyTqcTOzs7t7pPCCHY3NxEaWkp+vr6inYBk2sIIbBYLGnxqqenJ2NOlrOzM5SUlECj0WTk/Z4bX4fzi0QiKBQKiMXivDsjzWYzzs7OMD09nZexRKNRmEwmmM1mAIBSqYRKpeKsWzaZTGJ5eRlKpTIreU2pkuixsbEnvU+uSqYe4hK71VmRAb5srFBZWZlurJDvaytTJJNJfPz4Ee/evbvzd0q5yN69e0efq5QbMAyD9fV1lJWVYXBwkJ4fT8DlcsFoNGJoaOiXf7j5DwDDHwE1v3S/MiyLYDCAZJJBTU0NSh9b4RO1AcIq4Kd/T8smKU+CCmIUCoXzvH//Hm/fvi2aicpt7hOWZaHX6yEWi7MWHE25btRweHiIZDKJ7u5uiMXiJ51X5+fnEAgEaGpqyuAonye3hfPLZDIoFIq8lbRcXV3BbDZDq9Xm5P4TCoVgNBphtVohFAqhVquhVCoLJkMwJerzeDwMDQ1l/DP78z//8yeJGolEAnq9HlKpFB0dHVn9Tu/rEmMYBgsLC3j9OrtlP8FgEAaDATabDWVlZdBoNFAoFJxwQD6W7e1tiMXie7kGt7e3IZfLn3UDFMr3OTs7g9FohFar5ZRbuZAghODDhw949+5d6g+Aj/8hELHc2lmSYRgEggGwLEFNTQ1KHlr5kwgCST/w6g+Bato0g/J4qCBGoVA4z9raGjo7O4uqm1bKsTQzMwOWZbG0tISWlhbqNMoR4XAYR0dH8Hg8aG9vh1qtfpRr4vLyEoQQtNDOthmHK+H8Z2dncLvdGB0dzbiAQgiB3++/4eJRq9WQy+UFLVScnp7CarVCq9Vm9LtaX19HW1vbo0oLU81NXrx4AYlEkrEx3cV9XGJOpxNWqzWn5XyRSAQGgwEWi6UghVfgWjxeW1vDq1ev7nVdBgIB7O3tQavV5mB0lELE4/FgfX0dw8PDaGxszPdwChKdTofBwcHrMvRkCPjzvwAQPlDW8N3XJJkkAoEAAKCmphbC+z772CQQvgK0/wKQzGRi+JRnChXEKBQK5zGZTAiFQujq6sr3UDKKyWTCyckJWJZFf38/3bnOA8lkEqenpzAajVCpVGhvb3/QotBgMCCRSGSlRIzyS74Xzq9QKHLSWe/w8BCRSATDw8NPfq+UE85gMMDtdqOurg5qtRoSiaRoStmA69ym3d1dTE5OZiyjy2azweVyoa+v70GvMxqNODk5gVarRUVFRUbGch/u4xLb39+HSCTKW6faWCwGo9GYLs1VqVRQqVScd8ksLS2ht7f3QeLohw8fONssg8INUh1tFQoF2tvbi6YyIVcYDAZEo1F0dnYCcR/w/i8BPCFQKgIAEFxXRDBMEkySQZJJIh6Pp/9hWRatLa33OxhhgNAFMPHPabA+5UlQQYxCoXCeRCIBnU6H2dnZfA8lo4TDYfzZn/0Zamtr8dNPP9GJVx5hWRZGoxFnZ2eor69HV1fXvRbxJpMJ0WgU7e3tORglJcWX4fw+nw/l5eWQy+WQy+VZy9ja2dkBj8dDf3//g1/LsiwcDgcMBgP8fj8aGxuhVqvR0NBQ1Nd9IBCAXq/Hy5cv0dDwfYfAfWFZFnNzc3j79u29fp4Qgp2dHUSjUYyMjOTFdfcjl9j8/Dy0Wi0n3FnxeDzdvIFlWSgUCqjV6pyKiPfB6XTi/Pwc4+PjD3rd6ekpBAIBdfRS7oQQgt3dXYRCoazmDBYjiUQCCwsLGB4eRtDvRMPGb4JlGMRwM/aAZQlYlgEAlJSUXpdX8q7v8Y2NjeDdpw8lGwciJkD7/wbE1PlJeTx0i4RCoXCekpISMAwDlmWLxkHh8/mwurqKX/mVX4HD4cDKygrGx8eLenHMZfh8PpqamqDRaOB0OrGxsQGBQIDu7m6IRKLvvo7H44Fl2RyOlAIAAoEgLYABvwznX19fz1o4f39/PzY3N3F0dHQvtyrDMLBarekdc4lEgs7OTtTV1WVkPIVATU0NZmdnsby8jLa2tieXhPP5fJSUlCAWi/3QwRSPx6HX66FQKDA4OPik4z6Fnp4e6HS6WwUxQgiSySQnxDAAKC0tRUtLC1paWpBMJmGxWLC5uYlYLAa5XA6NRpP3zqaEEHz+/BlTU1MPfq1Go8Hy8jIVxCh3wuPxMDAwAIvFgo8fP2JiYiJvOZZchGEYhEIhBIPBG/9mmGuBy2g0ora2FrW1teBVaVAeOQNfWIl4PIZkMgmBQICKivL0Pdzr9aKsrAxVVVXweNzX2th9psLJCCCoBKpohivlaVCHGIVCKQh2dnYgk8lylv2STZxOJ7a3tzE9PZ12s5ycnMDr9WYlp4jyOFJd7UKhEDo7O6FQKL75bqxWK3w+H7q7u/M0SsrXZDOcnxCC1dVVNDY23lomm0gk0g6bZDIJuVwOtVqddxEh37Asi9XVVVRXV6O3t/dJ97izszPw+fw7RY3UhsPQ0BDEYvGjj5UpvucSCwaD2N/ff7DTKdekxF2j0YhIJAKpVAq1Wp2XXM+LiwtEIhH09vY+6vVLS0sYGhrKWBkvpbgJhULQ6XTo6em5V/OGYoBlWYTD4W8Er0QiAeB6Y6K6uhpVVVWoqqpK/3eqFHlvbw/JZBIsy6L26v8FefRnINXtKCsrg1DwSy9OPB6Hz+9HXV1dutOk2+NBXV0dBPfZyIqYgHI58PZ/v6eCRqHcDhXEKBRKQeB0OmGxWPK6058JzGYzjo+PMT09/Y0r4PDwEOFwGMPDw1QU4xCxWAwnJyew2Wxobm5GS0tLuoTCbrfD5XI9enFGyT5fh/PX1NSkxYnHZAkRQrC8vAy1Wg2NRoNoNAqj0QiLxQIAUCqVUKlUWSvdLFQIITg8PITP53tSGVI0GsX6+jqmp6dv/XuDwYDT09Oc54XdxfeyxC4uLgCgoBxLLMvCbrfDYDAgGAxCLBZDo9Ggrq4u68+tZDKJjx8/4u3bt48+fywWCzwez4Nz6CjPF4ZhsLa2hvLycgwODhb8/IwQgkgk8o3gFYvFAFw75FJi15eCV2lp6a3vx7Is3G43bDYbnE4nEokEwuEwXr16hfrICnjr/9V1l0nBtSOMAAgFg4gn4qivrwef90vxy+P1ora2BgL+D65vQoDgMdD+fwH6/q8Z+VwozxcqiFEolIKAZVl8+vTphy3suczFxQVMJhMmJye/O5lP7ay9ePEix6Oj/AiGYXB5eYmLiwtIpVJ0dnbC7/fD4XDQxVWBkKlw/kAggJ/97GcoKSlBQ0MDVCpVwXXpyxcmkwnHx8eYmpp6dHD7hw8f8Pr16xvlsIQQbG9vI5lM4uXLl5wrr9/c3IRSqbzRPGVlZQW9vb0FW45FCIHT6YTBYIDX60VDQwM0Gk3WsvF2dnZQX18PtVr96PcghOD9+/d49+5dwQsblNxydnYGk8mEiYkJTjedIIQgFoulha6U6BWJRNI/U1FRkRa6Uv8uKyu71zWR6o5stVpht9vBMAwaGhogl8vTMQXv37/H27dvwWPjwNyvX7u5KpvAEhZejxelZaWoqqr+JinM6/OhurrqhpPsVuJegAkD0/8SqKPzL8rToBliFAqlIODz+RAIBIjH49/dpeIyBwcHCAQCmJ6evnPC0dfXh8+fP2Nvb4+KLBxDIBCgra0Nra2tsFqt0Ol0YFm2YBezzxEej5fONunu7k6H819cXMDr9aKiouLWcP7UAsBgMMDhcKCyshKjo6M4OztDZ2dnUZRy5wqVSoXKykrMz89jbGzsUZlqYrEYTqczLS6lOsOpVCrOdnxNZYl9KYiFQqGCLqfl8XiQSCSQSCQghMDj8cBgMGBrawt1dXXQaDSQSCQZEZ7C4TBcLtejmlp8PeaGhga4XC5OlNNSCoe2tjbU19djfn4+Y41CHks8Hv8mxyscDiPlcykrK0sLXRKJBC0tLaioqHj0tRgOh2G1WmGz2RCNRlFXVweZTIa2trZbN4JEIhG8Xu91BmvLbwO7/xDxqA++YAJ1dbUoLbl9Hs/jAT/06hAGiNkB5a8CtdSdT3k61CFGoVAKhuPjY1RUVDxpdzjXEEKwtbUFgUCAgYGBe+++bW1toby8HD09PTkYJeWxXFxcYH19HVKpFF1dXRlb/FHyQyqc32q1IhaLoaSkBCzLIpFIoL6+HhqN5kZQfzKZxPz8PF68eJHXxVEhEolEsLy8jN7e3nRzhPvi8XhweXmJ4eFheL1erK2t5X2Beh++dIkVa/dk4FsBuaqqChqNBjKZ7NHOPZ1Oh66urjubnNwXn8+Ho6Mjzme3UbhJLBaDXq+HUqnMWofpZDJ5a3B9qolPSUlJ2t2VEr4qKysz5oxNRQ3YbDYEAgFUVlZCJpNBJpPdqxTdbrfDbrdjYGAAhInD97PfQqlPj3JxH/j87zup/QE/yssr0pli30AIEL4CysXA1P8HqCyc9QCFu1CHGIVCKRjkcjkODw8LRhBjWRYrKytoaGhAZ2fnvV/H4/EwNDSE9fV1nJycoKOjI4ujpDyF+vp6tLe3o6urC4eHh9jd3U130+NayRblx5SVlaGyshKlpaWIRqPg8XgoKysDy7KIRCLw+XyorKxMuwKFQiGmp6cxPz+P0dHRZ9VB8qlUVFTg9evX0Ol0CAQCD7pH1tfXY3NzM13CPDMzUxCZbV+6xFwuFxobG/M9pKzA4/FQV1eXvh4CgQAMBgMODw9RXl4OtVoNhUJx7xwwt9sNABkRwwCgrq4OwWAQDMM8OouM8nwpKyvD7Owsdnd3odfrMTo6+uDziGXZWwWvZDIJ4NqR/qXgJZfLUVVVlbXzNeWWttls8Hg8KCkpgVQqRXd3N6qrqx+80ScWi7G3t4d4PI6VlRXIZP8p2kv84IUvgapWgHf778G7tojd/qaEABEzwC8Bev4uFcMoGYM6xCgUSsFACEkH6nIdhmGwtLSEpqYmNDU9riU0IQQrKyuQSCRobW3N8AgpmcDv9+P09BQvX74EcL2re3p6CqPRCJVKhfb2dporxXEYhoHFYoHRaEQ0GoVEIoFGo/mmg14ikYDNZoPZbP4mnD+ZTGJhYQFarZaW0D4QQgg+f/6MRCJx7+wvlmXxR3/0R5DL5ZicnCwo8TnlErPb7enMnedEOByGwWCA1WpFSUkJ1Go1lErldxtcpJ77mW6ScHR0hPLy8kc/nykU4LpR0sHBAbRa7Y3yZ0LIrZ0aY7EYeDwe+Hx+enPlyxyvXM0XUt2YbTYbHA4HeDwexGIx5HI5RCJRRpzuP//5z8EwDEZGRq7vc55tYOPvAqFLoEINCL/t9BoIBlFaWoKy0q8y2tjktTNMWHkdot/0m08eH4WSggpiFAqloNDr9ejv7+d07ko8Hsfi4iJ6e3shk8me9F6EEOh0OiiVSjpx5yDBYBBHR0cYGRm58eeEEBgMBpydnaGurg7d3d2orPx28kfJD/F4HGazGSaTCclkEnK5HBqN5t7f0W3h/LW1tTCbzXj79i2n709c5eLiAgaDAZOTk3cuCmOxGHQ6HSoqKtJZcIVEquMkj8fDzMzMs3YopTq0ms1m8Pl8qFQqqFSqGzmhV1dXCAQCT84O+5pU2durV68y+r6U5wEhBNFoFKFQCE6nE1tbWxCJROmwfR6Pd2twfWlpaV5iFVLPrFQZZDKZhEgkSovymbwPEUJwcnKCg4MDdHZ23oz+CJwCu/8PwLl07fQqkwKCX7p7g6EQhEIByst+8WdsEoi7gIQPqOkAen8PkL3N2FgpFIAKYhQKpcC4urpCIpHIWm7DU4lEIlhaWsLw8HDG8mxYlsXS0hKam5sLplz0uRAKhbC/v4+xsbHv/ozD4cDR0RH4fD56enoyVvZDeRhfLr55PF568Z2JbmEMw8DpdOL8/Bz7+/vo6OiARqP5JpyfcjepheXk5OStoqLH48H6+jpGRkZQU1ODxcVFvH79Og8jfRobGxswm834K3/lr+R7KJwhHo/DZDLBZDKBZVkolUooFAosLy/j7du3WREOFxYWMDo6Sq9RyjcQQm4Nro9EIunQ9/Ly8rTQVVFRgePjY9TW1mJwcJATWaKRSCQdhB+JRFBbWwuZTAapVJq15lSJRAKrq6uoq6tDe3s7VlZWvs1JZBPA5f8KXP5/r91ibBIQlAGCSkRicfB5fJQJGYD5RVfMMgmg/CtAx38KlHE7J5JSmFBBjEKhFBSxWAxra2uYnp7O91C+we/3Y2VlBRMTE6ipqcnoe7Msi8XFRbS3t0OhUGT0vSmPJxKJ4PPnz5iYmPjhzwYCARweHiIUCqGzsxMKhYITk+ZiJhgMwmg0wmq1orS0NJ1dlM2yFK/XC71ej5aWFjidTsTjcYhEIigUihuB/JTbCYVC0Ol0GBwcvNG98/z8HAaDAVqtNi1ifvr0CVNTU98tt+MqNpsN79+/x1/7a38t30PhJMlkEmazGWtraxAIBOju7n6Qg/O+mEwmBAIB2rzmmZJIJG7t1JgKri8tLf0muL6iouLOe/jp6SnMZjMmJiYystnyEOLxOOx2O2w2G/x+PyoqKiCTySCXyzNabvw9Ug1OBgcH0910P378iNnZ2dsFbSYG2OcA1wrg3QbCBkSjYfB4fJTVKID6F4BoCJD/ChXCKFmFCmIUCqXg+PDhA968ecMpMcHlcmFrawtTU1NZm3gwDIOFhQV0d3c/uRSTkhlisRg2NjYwOTn5oNecnJzAZrOhubkZLS0tz7psKpMQQuDz+WA0GtPd7dRqNWQyWU4/Y5fLhc+fP6cXAm63G1arFU6nEwKBADKZDAqFguaNfYdEIoHl5WVoNBo0NTVhc3MTfD4fQ0NDN+77hdh5GABOTk5wdXWFgYGB9MKRcpNoNIrl5WXMzs7CZrPBYDAgGo1CKpVCo9FkZNOJZVl8+PABP/30UwZGTOEaDMPcGlzPMAyA66Yot3VqfOqzwu12Y2NjI+udb1POZKvVCo/HA6FQmO4EWVNTk7M5MiEEZ2dnMJlMmJiYuOG43N/fT5dm/hAmCsPlCRiWh5b2PoBDc3xKcUMFMQqFUnBsbm6iqakpqxONh2C1WnFwcIDp6ems2dBTpMK7+/r6brgnKPkhHo9jbW0NU1NTD34twzDpLnlSqRSdnZ0531EuBgghcLlcMBqNcLvdqK+vh1qthkQiyatobrPZcHh4+E1OVCqc32KxIBgM3gjnLzSnUzZJdem9vLyEVqtFc3PzNz8TCoWws7MDrVabhxE+nuXlZfT29mJzcxNv3rzJ93A4iV6vR3t7+41OnCzLwm634+rqCqFQ6EYDjMde6+vr62htbaWl7AUIy7IIh8NpsSsleCUSCQAAn8+/kd+V+ncu7rOpjDqlUpmxiA9CCDweT3pzBbju5iiTydDQ0JCX510ymcTq6iqqq6vR39//zRj8fj+Oj48xOjp6r/czGo2IxWKcjUWhFCd05kWhUAoOhUIBq9XKCUHs8vISBoMBr169yokDRSgUYnp6GvPz8xgaGuLEZ/Cc4fP56fKKhyIQCNDW1obW1lZYrdZ0UHh3d/c3HQ4pN0ktjA0GA4LBIBobG9HU1PSNgyifyGQyMAyD5eVlTE1NpctsUp311Go1CCEIBoMwm804OzsDy7KQSCRQKBSor6/nzO+SDzweD4LBILq6umAymaBWq7+5x1ZVVSEcDoMQUlCfVTQaRV1dHerq6mC326lL7Cs8Hg9Ylr0hhgHX91u5XA65XA5CCBwOB05OTuDz+dDY2AiNRvPgDnmtra04Pz+nghgHIYQgEol84/KKxWIAroPrv+zU2NTUlA6uzzdlZWWYnZ3Fzs4O9Ho9RkdHHzxHTD0fUjlgXwbhd3d3591Z7vP5sLq6ioGBge9WLdTU1MDv99/7Hi0QCNIOPgolV1BBjEKhFBxisRgHBwf5HgYODw/h8/kwPT2d01ygkpISzMzMYH5+HiMjI6ivr8/ZsSk3eYogloLH40GhUEChUMDr9WJvbw/JZBJdXV15dzlxCYZhYLFYYDQa06VTXBcPlUolkskk9Ho9tFrtN98lj8dDTU0Nuru70d3dnS6Buby8xNbWFsrKyqBQKJ5dOH+q/ObVq1coLS2F1WrF3NwcJicnvylJF4lE8Hg8BbM5EI1G007Qnp4e6HQ6Koh9ASEE29vbGB8fv/PneDwepFIppFIpCCFwu924urrC5uYm6uvrodFoIBaLf3j/FIlE2NjYAMuyNN8vxxBCEIvFbg2uT/Flp0a5XI7q6mqUlZUVxHORx+NhcHAQZrMZc3NzmJiY+GEH4kgkApvNBqvVikgkgpqaGsjlckxMTHBC6Etxfn6Oq6srTE9P3xkTwuPxUFtbC7/fj7q6uh++L5/Pp4IYJefQkkkKhVKQzM3N5a1lPSEEnz9/BiEEL168yNvELBaLYWFhAWNjY5wWBYoZQgg+ffqU8U53kUgER0dHcLvdaGtrg0ajeZaLtXg8DrPZDKPRCIZhoFAooFarMx6unW1OT0/h8XgwOjr6oPtFqkuY1WpFLBYr+nB+lmWxvr6O0tLSbzq1pZqWjIyM3HDzOBwOWK1WDA4O5mPID8ZoNCISiaCzsxPAdQSAUqmkotgvMBqN8Hg8j/4+UzmCBoMBTqcT1dXV0Gg0kEql371mDg4OUF1dXXBZdIXA150aU/+klp9lZWVpwStV1lhRUVEQgtdDCAaD0Ov16O3tvdEYKZFIwG63w2q1wu/3o7y8HHK5HDKZjJPPOYZhsLa2hvLy8nt300xlnPX29v7wZ51OJ2w2G/r7+zMxXArlXlBBjEKhFCT7+/uor6/PecdFQghWV1dRW1uL7u7unB77NiKRCBYXF7PS2ZJyP+bm5jIuiKVIJpM4PT2F0WiESqVCe3t7VjskcoFIJAKTyQSz2QwejweVSgWVSlXw+WoHBweIxWIYGhp61OtT+TEWi6Uow/kjkQh0Oh06Ojq+K0zEYjEsLS2hs7MTKpUKwPXn8vHjR7x9+zaHo308m5ubaGlpSTt7o9EodDodzRLDtSD6/v17vHnzJmM5T36/H0ajEXa7Pd2AQS6X39hMi0ajWFtbw8zMTEaO+ZxIJpPfCF7BYDDtnC4pKfkmx6uysrIoBf0fwTAM9Ho9WJZFTU0N3G43hEIhpFIpZDLZk7LwcoHf78fq6uo3ot6PYFkWnz59utc9zuPxwGAw4MWLF08ZKoXyIGjJJIVCKUgUCgXOz89zKoil8oDUavWtAc/5oKKiAlNTU1haWsLk5OQP7fiUwkIoFKK7uxtdXV0wGo1YWFhAXV0duru7Obl7/FiCwSAMBgNsNhtKS0uhVqsxPT1dVCHzPT092NnZwd7eHvr6+h78eh6Ph4aGhnRpYCqcf39/v+DD+Z1OJ7a2tjA2NnZnWU1ZWRlevXqF1dVV+P1+9PT0gMfjoaysDJFIJGsdfjOJz+e78TuWl5fTLLFfcHR0hLa2toyev7W1tejr60NfXx9CoRAMBgNOTk7S9xmFQoHy8nKwLItYLFbwwnumYVn2htCV+ncymQRwnfn0ZadGuVyOqqqqvOdbcQVCCLxeL6xWKxwOBwghYBgGgUAAb9++LZhS+MvLS5yfn2NycvLBcw8+nw+hUHiv6ysTMRQUykOhDjEKhVKQEELw4cMHvHv3LifHi8fjWFpaQldXV85dafchGAxCp9P9MM+Bknmy6RC7DafTicPDQ/D5fHR3dxdMdtKXpMqajEYjHA4HqqqqoFarIZPJinohRQjB5uYmqqur0yVzmXrfYDAIi8UCm81WUOH8JycnsFqt0Gq193Y/EkLSQuDY2BgMBgOSySTnO5OxLIv5+flv7hfUJXbt/ltcXMTbt29zcr5GIhEYjUZYLBbw+XyUlpaiurr6UWJ1IUMIudGpMfXveDwO4FqgqKysvCF6VVVVFb1T+Sl8GYSfSCRQX18PuVwOsVicFntdLhc2Nze/KQHnGgzDYH19HSUlJXjx4sWjnX1nZ2cQCAQ/3EwOBoM4PDy8d1dKCiUTUEGMQqEULEtLS3j58mXWd9gikQiWlpYwNDT0TdcrLpHK2JmZmSmYXcdiINeCWIpAIIDDw0OEQiF0dHRAqVRyWvgghMDlcsFgMMDj8aC+vh5qtfrZNQ4ghGBlZQUSiQStra1ZOUYqnN9iscDr9XIynD+VRVNZWYn+/v5HnQMGgwFnZ2cYGRnB1tYWZmdnszDSzJH6Tm7Lx3ruWWKrq6tobm6GRCLJ+bFjsRgMBgMWFhbQ1tYGpVIJlUrFmWvlKRBCEI1Gb7i8gsEgotEogGvnaUVFxTdljaWlpc/qvvwUotFoOgg/HA6nnXJSqfROR1QsFoNOp4NarUZbW1sOR3w/AoEAVlZW0NPTA6VS+aT3ikaj2NjYwNTU1J0/Fw6HsbOzg4mJiScdj0J5CIXlqadQKJQvkMlksFqtaGlpydoxAoEA9Ho9xsfHOR9cX1tbi7GxMSwuLmJmZoaWfhQ5NTU1GBsbQywWw8nJCQ4PD9HU1ITW1lbOuKxYloXdbofBYEAwGERjYyNaWlowPDz8bBdbPB4P4+PjWF5ehlAohEajyfgxUvliMpkMwC/D+Tc2NjgRzh8Oh6HT6dDV1ZXOAnsMGo0GVVVV0Ov1YBgGDMNw5ty/DafT+V3B5zl3nPT5fIjH43kRw4DrUtyOjg54PB60trYiFAphbW0NyWSS8408CCHp4PovBa9IJJIOri8vL08LXVKpFG1tbSgvL3+29+Cnkkwm00H4Pp8P5eXlkMlkGBgYeFBsRaoE/PPnz+mGIVy5f11dXeHs7AxarTYjURzl5eWIxWI/7OYqEAhoySQl51CHGIVCKVjC4TC2t7cxOTmZlfd3u93pHS2uToZvw+PxYHNzEzMzM5xq012s5Msh9jUMw+Dy8hIXFxeQSqXo6OjIi8MhmUzCarXCaDQiGo1CKpVCo9HQpg9fwbIsFhcX0d7entMy7K/D+fl8PuRyOeRyeU6+I7vdjs+fP2e0EUg4HMa//bf/Fv39/ZzuTjY/P39naehzdYl9+vQJIyMjec/ATDlYh4eHAVzfUy0WCwwGA2KxGGQyGTQaTc6bWCSTyRvljCnxKyUclJaWfuPwqqioeJbB9dmAZVm4XC5YrVa4XC4IBAJIpVLI5fKMBeGbTCYcHR1hYmIir9cBwzDY3NwEn8/H0NBQRs+hnZ0dSKXSO+9vyWQSOp2ONrig5BTqEKNQKAVLZWVlehc00zudqbDq2dnZgnNaiUQiDA4OYnFxEbOzswUXsE15HAKBAG1tbWhtbYXVaoVer0dFRQW6u7uz7m6Mx+MwmUwwmUxgWRZyuRwvXrwoKCE51/D5fExNTWF+fh5CoTBn7pjvhfMfHBxkNZyfEILj42M4HA68fv06oxlElZWV+At/4S/g3//7f4/y8nJOZokRQpBIJO78vZ+jS8xsNqO2tjbvYhgANDQ0YGtrKz2nEAgEUKvVUKvVYFkWVqsVe3t7CIVCkEqlUKvVdzaBuC8Mw9waXM8wDIDr5iopoau2thZKpRKVlZWccRMVG6mMS6vVCrvdDkIIGhsboVAo0N/fnxWhUaVSoa6uDjqd7sFdHDNFMBjEysoKOjs7v9vp9ymoVCpcXl7eeX+jofqUfEBXSRQKpaCpra2F3+/PyKQ0xdXVFa6urgpaTBKLxejr68PCwgJmZ2fpxPkZwePxoFAooFAo4PV6sb+/j0Qiga6urozmdX0dSq1UKjE+Pl5wAnI+EQgEmJmZwfz8PF68eJGXBgklJSXpRf+X4fxLS0sZC+dnGAarq6uoqanB9PR0Vkq1RCIRJBIJAoEANjY2OFeWm8oWuovy8nLU1tY+m46TLMtif3+fEw5b4PreKZfLYbFYvslMSt3jlEolWJaFw+HAyckJfD4fxGIxNBrNd68RlmURiUTS5Ywp8evL4Pqqqqq06CWRSFBdXV2w849CJBQKpYPw4/E46urqIJfL0dHRkbPvobq6Gm/evMHq6ipcLtejsxUfg9FoxPHxcVYdavX19djc3LzzZ6ggRskHtGSSQqEUNCaTCcFgEN3d3Rl5v+PjY7jdboyPjxdFuYHVasXx8TGmp6epKJYluFIyeReRSARHR0dwu91oa2uDRqN51PkdCARgNBphs9lQVlYGtVoNhUJBF25PJB6PY35+HqOjoxkV95/KbeH8crkcCoXi3uW4oVAoZ66HtbU1dHZ2wuVywWQyYXJykjPn5sXFBQghP2yk8Jw6Th4dHUEgEHDK0RcOh7G5uYnp6el7/TwhBE6nE2dnZ3A4HCgrK0N1dTV4PB5isRiAa6Hty06NXwbXU/JDLBZLB+GHQiFUVVVBLpdDJpPlfVOHEILT01NYLBZotdqsnicsy2JzcxOEELx8+TLr8169Xo++vr47Nwc+fvz4LO5/FO7AjVkChUKhPBKZTIazs7MnC2KEEOzs7IBhGExMTHDKWfAU5HI5GIbB0tISpqeni0LkozyciooKDA0NIZlM4uzsDO/fv4dKpUJ7e/udJVyp0hGDwQCn04mqqipoNBp0d3fTcymDlJaWYnp6GgsLC9BqtTnPKPoed4XzR6NRiEQiKJXK74bz22w27O7uYmJiIie/k1KphMlkQm9vL6qqqjA3N4fJyUlOlO46nU709PT88Oeei0ssHo/DaDTi3bt3+R7KDSorK5FMJhGPx9NCRCq4/uscr3A4nH5dRUUFWltb0/fMcDiM+vp6NDU1QSKR0PtlnkkF4dtstrS4L5PJ0N/fz4ly3S/h8Xjo6OiASCRK5+uJRKKMHycUCmFlZQXt7e1Zae5yGyqVCiaTKWOb2BRKJqCCGIVCKWiEQiFYlv1h55q7IIRgbW0N1dXVGBwczPAI849KpQLLstDpdJicnCwasY8rFJLRWigUoqurC52dnTAajVhYWEBdXR26u7vTokHK8WA0GuHxeFBfXw+NRoOBgQF67mSR8vJyTE5OYnl5GdPT06ioqMj3kL4htehPLfxT4fz7+/s3wvmrq6txeHgIj8eD169f58ylJZVKcXR0hN7eXkilUoyPj2NpaQnDw8NobGzMyRi+RzAYvPfCu7e3t+izxHZ2dnJaEvYjEolEWuji8Xj4+c9/jqqqqvT9vaysLO3sEovFaGlpQUVFxZ3j9/v9MBgM2N/fR0VFBTQaDeRyORXHckAqCN9ms8HlcoHP50MikaC1tRV1dXWcOe/uorGxEbOzs9DpdNBoND90lz4Ek8mEw8NDjI+P57ThjUwmw+npKRXEKJyCCmIUCqXgaWxshNPpfNTigWEY6HQ6KBSKjE42uIZGowHDMNDr9UXlgKM8Dh6PB41GA41GA6fTibW1NYTDYZSVlaUDhFtaWjiXw1TsVFVVYWJiAktLS5iZmcl76c5dfC+cf3d3F8fHx6ivr8fw8HBOBWOBQACBQJB299TU1ODVq1dYXl5GS0sLmpqacjaWL0kmkxAKhfe+lordJeb3+xEOh9POw1zAMMyN/K7Uf38ZXF9dXY3q6mq0tbVhZ2cHs7OzTxKvamtr011Pg8EgjEYjjo6OaLl5Fkg582w2G+x2O1iWRUNDA+RyOfr6+gpWhCwrK8OrV6/w+fNnrKysYGRk5EnxFyzLYnt7G8lkEm/evMl5lEbqPvijBiMUSi6hGWIUCqXgcTqdMJvNePHixYNel0gksLi4iM7Ozm8CdIuV09NTuN1ujI2NUaEjQxRChthtJJNJWCwWGI1GxGIx1NXVIRwOI5lMoqOjA0qlkp4jecLr9WJ9fR2zs7MFlTMUDAbTGTFVVVWwWCyw2WxgWRZisRgKhQIikSir59Xp6SmEQiGam5vTf8ayLNbX11FeXp4XV5LNZoPb7UZvb++9X1PMWWLz8/MYGhrKqDOFZdm02JUSvILBIJLJJIBrsfTLDK/Uf39PENDr9ejt7c2KeybVkMRsNkMoFEKtVkOpVFKB4IGEQqF0Dlg8HkdtbS3kcjmkUmlRCo0mkwlHR0ePDr4Ph8PQ6/VobW29cX/MNcfHxygvL/9umSbNEKPkGiqIUSiUgocQgo8fP+Lt27f3fk00GsXi4iJevHgBsVicvcFxkKOjIwSDQbx8+ZIKHhmgkASxeDwOk8kEk8kElmWhUCigUqluZCzF43GcnJzAarWiqakJra2ttCFDHnA6nWmXSiEs7qxWK/b29qDVar9ZrKW68j0lnP++RCIRbG5uYmpq6pu/Ozo6gsvlwsTExIPO6TgThy1oQ4yJQcgXQl4tR7nw/uPe2dmBTCaDRCK592sAYGNjAyqVqqhcYlarFRaLBS9fvnzQ6wgh6U6NXzq8vgyu/1LoSv33YwXl1Pn60I22hxKLxdLiGHAdcaBSqTjtDs0XsVgMdrsdVqsVwWAQlZWV6SD8TN9HuEogEMDKygr6+vogl8vv/bpUafvY2Bhqa2uzOMIfEw6H8fnzZ2i12lv/ngpilFzD/RkWhUKh/AAejwehUHgjBPcugsEgdDodxsbGONXRLVd0dXVhf38f29vbGBoayvdwKFkm5UawWCzg8/lQqVQYHx//7oKrtLQUfX196OnpweXlJT5+/AiJRILOzs5ns+jgAmKxGL29vVhcXMTMzAxnRUlCCPb39+H3+79bgsPn82+E80ejUVgsFmxsbCAWi6G+vh4KhSIj4eMVFRWIxWIghHwj+Hd1dcFsNuPTp0+YnJy883y2BW34P0/+TywaF3HgPEAoHgJLWPB4PFQIK9DZ0AmtWou/3PGX0VLfcueYHuoOS1FsWWKEEOzt7WF2dvbWv4vFYt8IXpFIBMD1c76ioiIteCmVynSnxmxs7IjFYnz+/PnW8yiTlJWVob29He3t7YjH4zCbzVhdXQXDMFAoFFCr1ZzME8wFyWQSDocjHYRfUlICmUyG3t5ezjQeyTU1NTV4/fo11tbW4HK50NfXd+f5SQjB58+fEY1G81IieRuVlZUIh8NZv7YolPtCHWIUCqUo+JEFO4XH48H6+jqmpqY40Xksn+zs7IDH46UzTiiPg4sOsUAgAKPRCJvN9uS8GkIIbDYbjo6OUF5ejp6enrzvMD8nTCYTLi4uMDU1xbkcnGQyCb1ej8bGRnR1dT1qcfNlOL/T6bwRzv/YcrXPnz9DLpd/15Hl9XqxtraG0dFR1NfX3/g7T8SDf776z/EnR38Cd8QNAV+AqpIqVJZUgs/jg4AgkogglAghwSRQW1aLn1p/wn+p/S+hrPm29P4xDuYvKSaX2P7+PqLRKMRi8Y1OjamlSHl5+Q2XV3V1NcrLy/O2aN7Z2YFEIslp1lmKr0va5XI5NBoN5zoiZhKWZeF2u2Gz2eB0OsHj8SCRSCCXy1FfX0/Fky8ghOD09BRWqxUTExO3bgZHIhHo9fq005tLbG1tQa1W39rshDrEKLmGCmIUCqUoCAQCODj4/7P3p8GNdft5H/psACRmkCBBAAQBziQ4NofmPPRglSJbdWRNdvlLJPtTYudWJZIlq5xKKrbipCxbcqxUYktl3bIrUmTrWrZ0jmSVZMnnvD1wBIcm2WQ32ZwJkMRIYh733ut+4AFOszk0B4AYuH5VqH5fEsMisLH3Ws96/s9/Hf39/Vfex+l0Ym1tDSMjI7QcAWcTquXl5ZTIQbkbb968wfj4eFYn64QQeL1eWK1WeDweyOVymEwm6HS6tIooXq8XGxsbiMfjMJvNqKiooIuUB2B/fz+18MmV9zsQCMBisaCzszOtYk0ynP/4+BjBYBAKhQKVlZXQ6XQ3zljyeDyw2WzXOmCj0ShmZmbQ3NycypCcPJjEP574x9j0bKJUWopyaTmEgqsdFTzh4Y164Q67YVQa8XNDP4dvNX/r3Gfk8/mws7Nz6xLBz8eZL1liLMuey+9K5nnxPA+O41KfSVLsUigUkEqlOSf0JgmFQnj//j2GhoayOg6O4+BwOGC1WhEOh6HVamEymfJ+Y4IQAr/fD7vdDqfTCY7jUkH4Go0mZ4+LXMLj8WBpaQm9vb1Qq9WpnzscDqyuruZsJcR12b9UEKM8NFQQo1AoBcM333yDly9fXvo7m82G3d1dDA8P50Uez0NBCMG7d++gVCrR1NSU7eHkJRMTExgeHn7wUgRCCNxuN2w2G05PT6FWq2E0GqHRaDIumkQiEXz69AknJyeor6+HyWSii5cMs729Da/Xi97e3qyLYkdHR9jY2MDg4GDGnbaBQODW4fyEELx69erK60ESjuMwNzcHtVqNHcEO/uHrf4hALIDqkmoUCW8ecM7xHGx+G4SMED839HP42a6fTY1ta2sLYrH4q+7l68gVlxjHcQiHwxfKGj8Prk/md32e4yUUCrG0tJRy/uUTr1+/xujoaM7MG5J5fAcHBwgEAtBoNKiurkZJSUnWzws3IRwOw263w+FwIBqNoqSkBDqdDlqtljYVuCOxWAyzs7MwmUyora3F2toaQqEQnj59mjPH7Zdcd47OhU1GyuOCCmIUCqVgsFgsaG9vv1BSsL29DZfLhYGBAbpovwRCCObn51FeXo76+vpsDyfvmJycxODg4INMPHmeTzkFQqEQNBoNjEZj1spJWJbFzs4OrFYrDAYDGhsb6aImg6yvryMWi2Ut+48QgrW1NYTDYTx9+vTBReDbhPPPzs6io6PjqyVmhBD87pvfxa+8+xUIxAJUl1Tf+btkD9qR4BL45Re/jB9v+XEAwMzMDLq6uu6VAxWNRmGxWDJems3z/KXB9fF4HMBZFtyXwfUKheKr575gMIh3795hfHw8o+PPBDs7OwCQk9fG5KaI1WqF1+tFWVkZTCYTysrKckZMiMfjqU6QySD8ZJ7gY81GywSEECwsLGBzcxN9fX15scE5MzODJ0+eXNhUydYmI+XxQgUxCoVSMBwcHCAej6OxsRHADwJ8Y7EY7aj4FQghmJ2dRWVlZVbbcecj09PTePr06Z27mX2NL7NkdDodjEbjnfOVMgEhBDabDdvb2ygpKUFzc3NBZ91kk/fv30MoFKKtre1BXzeRSGB2dhY6nQ6NjY05cT5NhvPb7XZEo1Go1epUOP/R0REikchXF4a+qA8/++2fxUf7R5QLy1FWVnavjZMD3wHUEjV+5yd/B6YSE169enXn/LDPSYdLjBCCaDR6QfCKRqMAfhBcnyxn/LxT430+78nJSXR2duZliR/LspicnMz5Eq5kFl+ybL6kpAQmk+nBy9o5joPL5YLdbk8F4Wu1Wuj1eigUipw4bxQiTqcT79+/h8FggN1ufxD37n2xWq2IxWKpOXuSTM+pKJQvyU0fJYVCodwBnU6H+fl5NDY2ghCCxcVFSKVSKobdAIZhMDAwgJmZGQgEgnuV9zw2BAIBeJ5P63PGYjEcHR3h8PAQPM+jsrIS3d3dObujzjAMTCYTTCYT3G43lpaWIBAIYDabUVZWlu3hFRQdHR149+4dNjc3H8wF4Pf7MTc3hydPnlwZVJ8NJBIJ6urqUFdXdy6cf319PfX/Xwvn/63F38KGewP1mnoQjuDEc4JSdemdHZ9GlRFbJ1v4P6b/D/zjF/84bXmVra2tsFgs1wpihBDE4/Fz+V3BYBCRSORccH1S8NLr9ZDL5RkNrnc6nZBIJHkphgGASCSCWCxGKBTKaZGfYRiUlZWlzrc+nw9WqxVra2sZy5QEzo65k5MT2O32VBB+soyzq6uLzr0yTHLj1+/349mzZygqKoLRaMTMzAza2tpyukRZr9djZmbmgiCWiTkVhXIdVBCjUCgFg1gsRiKRAMdxsFgs0Ol0OVnmkKsIBAIMDQ1hamoKQqEwFTRNuR6GYZAOs3UkEoHNZsPx8TEEAgGqqqqu7B6Vy2g0mlQXuY2NDbx//x6NjY0wGAx0cZQGGIZBT08P5ubmsLu7m/HuYTabDVtbWxgZGclZQRa4KAgkEgn82Z/9GdbW1hCJRC4N5/eEPfjO+negEqvOMsOEgLpMjdPTUyiVyjuJWQJGAK1ci7cHbzG7NQuDJj3nUYlEAqVSiaOjI8hksnMur3A4nFpAFhcXp9xd5eXlqK6uhkwmy8p3jxCC1dVVjI6OPvhrp5O6ujrs7u6io6Mj20O5MSUlJakw9WTX4Y2NDUgkklTX4buUpBFCEAgEUkH4LMuirKwMOp0OLS0ttMztAYnFYrBYLKisrMTQ0FDqO65UKvHs2TPMz8/D4/Ggra0tJ6+9RUVFIISA47hzx41AIADHcVkcGeWxQUsmKRRKQbG4uAin04nOzk5UVVVlezh5CcdxmJychNlszkq7+Xxjfn4era2td3IPBAIBWK1WOJ1OiMXi1EIlV4Nw70I8HsfW1haOj49RU1ODuro6umhKA4QQzMzMwGQywWg0ZuT5379/j1gshqdPn+Zl/uLGxgYUCgWqqqpSi3iHwwGO46DRaDAVmMI/m/9nqFfXn+smSXiCU+8pJGIJZPLblx0RQrB5solv6b+F//mH/udz3d++Bsdx58oZk/9yHAeWZVOd2b4Mrs/Fz2dnZweJRAJmsznbQ7kXhJBU055cFBZuQzgchtVqhd1uh0gkgslkQmVl5bXZj5FIJPXdiUQiUKlUqSD8fNuwKRRcLhdWVlYudJf8HEIItra24HA4cnZzbWNjA0ql8twG7OLiIhobG/PWVUrJP6ggRqFQCoZoNIr/8l/+CzQaTdbbpOc7ydyUtra2nCqRykUWFxfR1NR0o0wvQgi8Xm8q50WhUMBkMkGr1ebkgjad8DyP/f197O7uoqKiAk1NTReC0Cm3g+d5TE5OoqmpKa2lMfF4HLOzszAYDGhoaEjb8z40wWAQHz58wMDAwLmfJ8P5f+HPfwFv7W9hkpkgloghEUsgEH7/e0gAn98HBszZwuyWOojNb4MiocCb/8+bcyIKz/OXdmpMJBIAzjo1fhlcL5fLUyJ5rnSc/Bosy+L169d4+fJlQZzbVlZWUvl0hUI0GoXNZsPR0VHKlZzcSHQ6nXA4HPD7/ZBKpdDpdNDr9TntEn0MEEKwvr6O09NT9Pf336iJjdvtxvLyMp4+fYrS0tLMD/IWBAIBrK+vo7+/P/Wz5eVl1NTU5NxYKYVL4WxBUyiUR00oFMLMzAyGh4extraW7eHkPSKRCKOjo5iYmEBnZyfKy8uzPaScRSAQXFsy+WUnMLVaDZPJhM7Ozrx3G9wGgUCAuro61NbWwuFwwGKxQCKRoKWlhe4E3xGBQICRkRFMTExAKBSmZbHu8/kwPz+Prq4uaDSaNIwyeygUCoRCIRBCzn3XBAIBKrQVcMCBipIKlMhKEI1F4fP5wPEciouKIZaIoVKpEA6HcXJ6AnWpGozgBt9XAnA8hyJSBGfEial3UxDGhYjFYgDOSjs/F7zKyspSwfU3obW1FbOzszkviH348AEtLS0FIYYBZ2WTHz9+LChBTCKRoLGxEXV1dansvbdv34JhGFRXV6OtrQ0ajeZRXadymVgshrm5OWi1WgwPD9/4c9FoNBgdHcXs7CxqampQW1ub2YHeAqVSiWAweO4cLRQKackk5UGhghiFQsl7vF4vFhYWMDQ0BLlcDoZhwLJsQZWdZQORSJRabPf09Nyq7OcxwTDMhQBYnudht9ths9kQCoWg0WhQX1+PkpKSR7+4YBgGer0eer0eXq8XHz9+RDweR3NzM7Ra7aN/f26LUChMfU+7u7vv9T21Wq3Y3t7G6Ohowbj3SktLU0L053jCHviiPkiLpBAIBZDJZGdd2chZ/lg0dtaNkQEDgUAAl9uF8vJyCAVC8DwPlmPBsVzqX47/wQJOKBCCcAQsw4KTc3ja9hRisTgtx3YyoN7pdOasKBYKheD1etHZ2ZntoaQNpVKJcDh8Ie8oH0k2nEgG4QNnoklPTw9+6Id+CBzH4ejoCJubm/jw4QP0ej1MJlPOdy0sZJIur+7u7jttUEokEoyPj2NlZQXz8/Po7e3NGbG6tLQUPp8v5QijofqUh4auFikUSl7jcrlSob3JBVxFRQVcLhcqKyuzPLr8p7i4GKOjo5icnMTTp09TIb2UH5CcvCXzfQ4PDxGLxaDT6dDW1gaFQpHtIeYspaWlGBwcRCQSwadPn/DhwwfU1dWhuro6Zybr+UBRURFGRkYwOTmJvr6+WzvuCCFYWVkBy7J49uxZQb33VVVVODo6uiCIxbk4CCEQMF/8rQxQVFyEouKzUiSe5xGPxcHzPA4PDyGRSFBUVASRUAShSAhxsRhCmfAsg+wzvevEd4JiUgy5Up52cTHXXWIrKyt48uRJwYnbRqMRNpsNNTU12R7KrSCEIBgMpnLAWJaFWq2GXq+H2Wy+IPCJRCJUV1ejuroaHMfBbrdjZWUF0WgUWq0WJpPpRhEBlPtDCMGnT5/gcrkwPj5+rxwwgUCA7u5u2Gw2vHnzBgMDAzkhclZVVeHw8DAliFGHGOWhoYIYhULJWw4PD7G9vY3x8fFzbjC9Xo/d3V0qiKUJsVicWmwPDAzQifBnxGIxuFwuHB0dQSqVwmAwoLu7m+as3BKpVIquri6wLIvd3V188803qfyqXAwCzkXEYjGGh4cxNTWVcsvehFgshtnZWZhMpox3rMwGGo0GHz58uPDzImHRmbuTXO9EEAgEkEglkEglICAghEAqkUIivV7kisfjKCoqOutemWZy2SXmdrshEokKMv+nuroaMzMzeSGIRSIROBwO2O12RCIRKJVK6PX6W4erC4XCVLYYz/NwOp1YX19HMBhERUUFjEYjdT5niHg8jrm5OZSXl2N0dDRt73HyM5uZmUF7e3vWmyd9eY4W8Dy4cBggBKDHFeUBoIIYhULJS3Z2duBwODA2NnbBzVBSUgKfz5elkRUmEokEw8PDmJ6evtViuxAJh8Ow2Ww4Pj6GUCiEQCBAZ2fnuS5JlLshEonQ1NSExsZGHB4eYmpqCiqVCmaz+VEfczdFKpViaGgIMzMzGBkZ+aowmyw37+npQVlZ2QON8mERCAQoLi5GNBo959Qql5ZDJVbBG/WiVFJ6o+cSiUSQSWUIhoJgORYKueLysH0CRLko5DI5DMrMnBdy0SWW7Ew6PDyc7aFkhOLiYohEIkQikZzb9EgkEnA6nbDb7fD7/ZBIJNDr9Xjy5EnaXEACgSBV7k4Igcvlws7ODrxeL8rLy2E0GlFWVkbFsTRwcnKCd+/eZSzLUalU4tmzZ5ifn4fH40Fra2vWPjdBIAD99DQS3/0uij58gHFvDwKGAWQyoLER6OwEBgeBgQGARqFQMgDtMkmhUPKOjx8/IhwOo7e398oL+PT0NHp6egomBydX+Lx5QS5Y7R8Kv98Pm80Gp9MJiUQCo9EIvV4PkUiE9fV1qNXqrO+yFiputxufPn0CwzBobm6mDR5ugN/vx/z8PEZHRyEWiy+9z/7+Pvb29jA4OFjw58m9vT0QQi444P6HP/sf8Bfbf4GGspt10gwFQxCKhJCIJQgEA+A47lJ3DMdy2HZto9XQij/8G3+YsYVmrnWc3N/fRzgcRmtra7aHkjGOjo7g9XrR1taW1XFwHAePxwO73Y6TkxOIRCJotVrodDqoVKoHFTcIITg5OYHVasXJyQlKS0thMploIP8dIIRga2sLdrsdAwMDV56/0/l6m5ubcDqdt3YP3pvTU+Df/Bvgj/4IcZsNIATFSiWi39/klohEQCQCJBJAcTFgNgP/9X8N/NiPAQVU1k/JPlQQo1AoeQMhBEtLSyguLkZbW9u1E63d3V0wDJNT3XQKhUAgAIvFUlDB219CCIHX64XVaoXb7YZSqYTJZIJWq73gSNzY2EBJSQn0en2WRvs4CAaD2NjYQDAYRGNjIwwGA11sXYPX68W7d+8wNjaGoqIflO3xPI/l5WUAQFdXV0HlhV1FLBZLCYSf8/+u/L/43978b6hX159lgH2FaCQKjuMgV5y5FSORCMLhMNRq9bn3MRwKY9e/i/9u6L/DL478Ynr/mM/HE41idnYWz58/z9hr3BSWZfH69Wu8ePEi70Pnr4MQgm+++QYvX758cNHJ6/XCbrfD5XKBEAKNRgOdToeysrKc+R4TQuDz+VLXToVCceW1k3KeRCKBubk5qNVqtLS0POjxlQztf/r06cOUO795A/zarwEbG4BSCa68HL5gEGVlZYhEIuAJgfzzTddwGHA4zv77h34I+KVfAqqqMj9OyqOACmIUCiUv4HkeFosFGo0GjY2NX71/JBLB8vIyhoaGHmB0jw+fz4eFhYVrHSj5RrIExGazpbrSmUwmlJeXXzsx3dzchFwupyWTD0Q8HsfW1haOj49RU1ODurq6gl6A3we3243V1VWMjY1BJBIhFothZmYGtbW1eZGDlE5evXp1oWGAK+TCT/z/fgIJLgGd4usOz0QigUg4AlXJD5oWxONx+P1+qEvVEIrOjkObywZSRPDv/tq/Q2tFZt1SueISW11dRUlJCUwmU1bH8RC8e/cO1dXVGXWrJoPwHQ4HHA4HEokESktLodfrodFo8qaLdiAQgNVqTbmrTSYT9Ho9PWd/wenpKRYXF/HkyRNUVFRkZQxJgb2mpiazm8m///vAP/2nZyKXyQR8f8PG4/GgrKwMsVgMLMdBcVlMQiAAHB+fucX+2T8DmpoyN07Ko4EKYhQKJedhWRbT09Oora291WT7m2++wYsXL6iLJEOcnp6mHCj5GnzOcRwcDgdsNhtCodCdQoK3t7chFothNBozPFrK5/A8j/39fezu7kKj0aC5ublgHYv3weFw4NOnT2htbcXy8jJ6e3svdFx8DHz48AHl5eUXSpt/ZeJX8G+W/g3q1fUQCa4XGQhPcOo9vZC3xrEcTr2nUKlUEBWJ8N72Hn+1/a/i//rR/yvj159ccIlFIhFYLBY8e/bsUVxvfT4fNjc30dfXl9bnjUajqSD8cDgMhUIBvV4PrVZbEBtPoVAINpsNdrsdRUVFMBqNMBgMeSPuZQJCCLa3t3F0dISBgYGsX8N4nsfKygo4jkNPT0/6XX1/8ifA//K/nAXmGwznQvMDgQCKi4tBALCJxNUdulkW2N0FmpuB3/gN6hSj3BsqiFEolJwmFothamoK7e3tt94BX1xcRENDA0pKSjI0OorH48H79+9TDpR8gGVZHB0dwWazIZFIQKfTwWg0Xj35+gq7u7sQiUSPwhmRixBC4HA4sLm5CbFYjJaWFqhUqq8/8BExNzeHjY0N/PRP/3TOhYE/FF6vFzs7O+jt7T3385PICX72D38Wu6e7qFPXfVXQ8Xg8lzqDeJ7H6ekpvLwXxaQY3/6b30ZtaW06/4QreffuHYxGY9acJbOzs2hubn5UQus333yDZ8+e3cvpxLJsKgjf5/NBIpFAp9NBp9MVfBORaDQKm82Go6MjCASCVCfLfN1cuwssy2J+fh5KpfKrMSAPjc1mw9bWFgYGBtKXF2u1Aj/zM8DJCXCJQzmRSCAcDkMilSIei13f0ZxlgZ0d4C//ZeCf/3OaKUa5F/mxeqFQKI+ScDiM6enpOzsa9Ho97HY7FcQySHl5Odrb2zE5OYnR0dGcFcVisRgODw9xeHgIQggMBgN6enrSIg4wDAOe59MwSspdYBgm1fnM5/Ph48ePiMfjaG5uhlarzalFxkPD8zzevXuHoqIijI2NYWVlBQMDA4/yPbmq+3CZtAx/f+zv4+/+578Lm98Go8p4p/dHIBCAFbOI+WL46aqfRk3Jw5Wktra2wmKxZEUQOzk5AYBHJYYBQFVVFY6Ojm61EcLzfCoI3+PxQCgUQqvVoqGh4cGD8LONRCJBY2MjGhsbU9fn2dlZEEJS4li23VKZJNnht6OjIycb8hiNRqhUKszMzKC9vf3+YyTkrEzy+Pisc+QliIqKkEgkIJVK8VW/jkgEVFYC3/0u8Md/DPz4j99vfJRHDXWIUSiUnMTn82F+fh6Dg4N3du4kSy3Hx8fTPDrKlzgcDmxsbGB0dDRnskHC4TBsNhuOj48hEolQVVUFg8GQ9h3og4MDcBx3oYMdJXtEIhFsbm7C7Xajvr4e1dXVjy7QOVlKV1dXh+rqagDA1tYWfD7ftR16C5m5uTm0tLRc6jz4440/xj96848QiodQXVp9ZfnkyckJ1Gr1ufePJzxsfhsA4CcMP4Ef1f0oAKC/v//BjrtsuMQIIXj9+jUGBwcfnfMwFovBYrFcO79IBszb7XY4nU4QQlBeXg69Xp9TQfi5RCKRSDm4WZZFZWUljEZjQXW13tnZgc1my4kSya/Bsizm5uZQUlKC1tbWu183VlaAv/W3AKUSuMbB7fV6IZZIEI/HUXITp/fuLtDSAvze752JZBTKHaCCGIVCyTncbjdWVlYwMjJy78nC69evMT4+TieeD8DR0RF2dnYwMjKStffb7/fDZrOlAnyNRiMqKyszKtLZbDbEYjE0NDRk7DUod4NlWezu7uLg4AAGgwENDQ2PoiTH4/FgaWnp0o5h6+vriMfjePLkSXYGl0WOjo7g9/vR0tJy6e9f7b3CP5n8J9g53UGZtAxl0jIImPPnMp/PB7lMDlGR6KzzX9QLV9iFSkUl/vvB/x7lznKMjY3h+PgY29vbGBoaepD8p2g0msrxeiisViv8fj/a29sf7DVziYmJCfT19Z2bp4RCIdjtdjgcjrNF/fc7EFdUVOSsgzpX4TgOx8fHsNlsiEaj0Ol0MJlMd94kzTYsy2JhYQEymQwdHR15sylBCMHm5iZcLhcGBgbOdS2+Mf/7/w78zu+cucOu+buj0ShisRgA3Ky6IxwGXK6zLLEvughTKDeFCmIUCiWnODo6wubmJkZGRu520f2CtbU1VFRUZL0D12PBarXCarVieHj4QSZ7hBCcnp7CarXC4/FApVLBaDQ+aIv3w8NDRCKRG3U/pWQHQggODw+xtbUFlUoFs9lcsBk9yYDmwcHBK8W/9+/fQyQSobU1sx0Qcw2O4zA5OXmtaOQOu/EvLP8Cf7r1p/BGvSgSFkFeJIe0SAohI0QwFEQCCbAMiygbhapYhbHqMfz88M/DpDLh9evXePHiBYAfdI4bGBi4Pg8nTTykS4zjOLx69QovXrzIGVfwQ2Oz2XB6eoqSkhLY7XaEQiHI5XLo9XrodLqCCMLPFXieh8PhgNVqTTXAMZlMeROJkax6aG9vh16vz/Zw7oTb7cby8jL6+vpu975zHPAjPwJ4vWdljtdACIHb7UZRUdGFzZwr2dwE/tv/Fvj5n7/5mCiUz6CCGIVCyRn29vZweHiIoaGhtE2wPR4PDg8PH6UbIlvs7+/j+PgYg4ODGRHFCCFwuVywWq3w+XwoKyuD0WhEeXl5VnZcj4+PEQgE0Nzc/OCvTbk9Ho8HGxsbAACz2XxpQHo+wvM8FhYWIBaL0dnZee13gRCCd+/eQaVSPToh9+3btxgaGvrqhsuh/xB/uvWnmDyYxObJJiKJCDjCgWVZFDPFaNG3YLBqED/a9KNoKm8CcLbo3d7ePhfcHw6HMTMzg46OjoxvzDykS+zDhw+Qy+WouSQcu5BJBuE7HA6cnp7CZrNhdHQUer2+YEX2XOPLOUB5eTmqq6tRWlqak66rpEt5YGAg70uLk6X4tbW1N//u7+0Bf+2vAXL5teWSSdweDwQMc6Gb77XP398P/Ot/fbP7UyhfQAUxCoWSE6yvryMQCKCvry+tE5pkxklyx57yMOzs7MDj8aTt8+Q4LrU7HA6HU7vDuRBEnFwYXVWGRclNQqEQNjY2EAgE0NDQgKqqqqwfS3clEolgdnYWjY2NMBqNN3oMIQRzc3PQarWora3N7ABziK2tLYjF4luFoUcSERwGDpHgEggHw4h5YhjpG7lwv+3tbRQXF194bpZlMTMzg6qqqoxnDT6ESywajWJmZgbPnz/P2+/MTUkG4TscDng8HggEAlRUVECv16OkpASLi4uor69/dE0FcgVCCDweD6xWK05PT6FWq2EymbK2QfY5HMed26QolOgOnuexvLwMnufR09Pz9b/rzRvgb//ts86SNygbDgaDCIVCNw/ydzoBsRh49Yp2m6TcCVrMTqFQsgohBMvLyxAKhWkXw4CzDnQikQjxePxRZAflCvX19eA4Du/evUNPT8+dPtdEIpHKD0kkEtDpdGhvb8+5/BCGYb7eEYmSc8jlcvT29iIej2NrawsbGxuorq5GXV1dXmX9uFwurKysoL+/H6qbhBB/H4Zh0N/fj+npaYhEohsLafmOwWDAysrKrQQxaZEUjWVnTrp4SRxzR3OX3s/lcqGrq+vCz0UiEUZHR/Hu3TsEAoGvOvjuw0N0nFxZWcno35BNkkH4DocDTqcTPM+jrKwMer0ebW1tFxb/9fX12N3dpYJYlmAYBhqNBhqN5izTz+uF1WrFysoKVCoVTCYTKioqHlyM8vv9mJ+fR0tLCwwGw4O+dqYRCATo6emB1WrFmzdvvt5UIxYDeB64pvKD43mwLAs2kUAsFrtd526hEGBZIB4HcrxJASU3yZ8ZH4VCKTh4nsfc3BzKysrQ1NSUsdfR6/VwOBy3WgBR7k9TUxPW19exsrKCJ0+e3GjxlGy/fnh4CEIIDAYDent7c7oTk0AguN3kjZJTFBcXo62tDS0tLdjf38ebN2+g0WjQ3Nyc08cdIQTb29uw2+149uzZnTIXGYbB0NAQJicnIRKJ8jbb5jbIZDJEIhEQQu4k6BQXF4Nl2Ut/F41Gr1wYMgyD3t5ebG5uYmZmBgMDAxnJ3pJIJFAqlXC5XBkRxbxeL3ieL5hSY+B8EH4sFksF4Tc0NHxVHC8tLU29J4XiAMpXGIaBWq1OiZN+vx9WqxUfP36EVCqFyWSCTqfLeObdwcEBdnZ2MDQ0VFDdMb8kmeE2PT19fUm4QAB8f+OQ43mwiQRYlkWCZcFzHACAEQhQJBJBIBSC5ThoNJqbD4SQM1EsjzayKLkFPXIoFEpWSJaQVFdXo7q6OqOvpdfr8fHjRyqIZYGWlhasra1hbW0NHR0dl94nHA7DarXCbrennCrXBYLnGlQQKwwEAgHq6upQW1sLp9OJubk5iMVimM3mnAtuTpbiyGQyjI6O3supIxAIMDIygomJCYhEotstRPKU8vJyeDyetP6tsVjsRiHqTU1NUCgUqSyzTIiumXKJJR3d/f39aX3ehyYWi8HhcMDhcCAYDEImk0Gv199p84VhGBgMBhwfH6OqqipDI6bcBZVKleqAGgqFYLVa8enTJ4jF4lQH6nS6gZOueJFIhGfPnj0KgVSlUuHZs2eYm5uDx+NBS0sLeJ6H3+9P3dijIzQlEkjY7WBkMoiKiiAqKoJUKoVAKETy6sUTghOPB2q1+nYbPNEoUFdHBTHKnaFHDoVCeXDi8TimpqbQ2tp684yAe6BQKBAMBjP+OpTLaW9vx/LyMj5+/JjqapfcuXU6nZBKpTAajRgbG8vLbmW0ZLKwYBgGOp0OOp0OPp8P6+vriMfjaG5uhlarzXqZWDgcxuzsLJqbm9O2ABcKhSlRrLu7u+DLvwwGA46Oju4siAkEAnAcd+585Xa7b/x8lZWVkEqlmJycvH3HthuQKZfY0dERysrK8s71wrIsXC5XKu+xuLgYOp0Ora2taSnBr62txcLCAhXEchi5XI6Wlha0tLQgEonAZrNhamoKQqEQVVVVqKqquldn82AwCIvFArPZ/GiOg1gsBp/PB7/fD7FYjI8fP2J+fj7V4EClUsFgMEBVXY2i3/zNs7LJKzpHEkJwenICpUqF4tt+DtEoQBtnUe4BFcQoFMqDkuy41d3dffMOMmlALpcjGAzmXP7UY6GzsxMTExM4ODhAUVFRKtujtbU173dRqUOscCkpKcHg4CCi0Sg+ffqEtbU11NfXo7q6OivHrdPpxPv37zEwMAClUpnW5y4qKsLIyEhKpLlNHlm+UV5ejvfv39/58XK5HOFw+Nxn4HK5btVxsbS0FKOjo5ienkZra2vay1XT7RLjeR7r6+t4/vx5Wp4vk/A8j5OTEzgcDrjdbjAMg4qKCtTU1KCrqyvtorZEIgHP8zd2CVKyi1QqRVNTE5qamhCLxWCz2TAzMwPgTCw3Go23+hytViu2trYwODhYcJ1GCSEIhULw+/0p8SsajQIAxGIxVCoVVCoVtFoturu74fF4sLKygtra2vNCf2fnWbj+JZsGBMCp1wuZXA7xbSsDkuXrnZ13/AspFNplkkKhPCB+vx9zc3MZWcx9DavVilgshsbGxgd93cfMl63Ry8rKcHp6CpPJVFCfQyAQwObmJnp7e7M9FEqGYVkWu7u7ODg4gMFgQENDw4OU9hJC8OnTJ7jdbgwODmY09D8SiWBqagpDQ0MFt7j7nJmZGTx58uRObqeNjY1UzlSS169fY3x8/NZCKcdxmJ2dhVarTft5MZ0dJ9fX1yEWizPeJfMuEELg9/tht9vhdDrBcVwqCF+j0TyIeH1wcIBYLJbRPFRKZonH4zg6OsLh4SE4jkNlZSWMRuOVuYA8z2NpaQkA0N3dndebeyzLIhAIpISvQCAAlmXBMAzkcnlK+FKpVJBIJNeKypFIBBaLBbW1tT/YJPjTPwV+4RcAkwn47JpJAPi8XhQXF9/NeWq3A3I58J3vADkWbUDJH6ggRqFQHgSPx4Pl5WUMDw9f340mQ8TjcczNzWF0dPTBX/sxwXEc7HY7bDYbIpEINBoNTCYTVCpVqrTQYrFAp9OhtrY228NNC6FQCOvr63j69Gm2h0J5IAghODw8xPb2NpRKJcxmc8bEI47jMDc3B5VKhdbW1gcp2QyFQpiZmcHIyEhWztcPwcHBAeLx+J1EKJvNhlgshoaGBgBnC+O3b9/e2T1FCMHKygp4nkd3d3faPuNoNAqLxYJnz57d63lisRimpqbw4sWLrJcMJwmHw6kg/Gg0ipKSEuh0Omi12nuVvt0VjuPw9u1bvHjx4sFfm5J+WJZNdbmOxWLQ6/UwmUyp83woFILFYkFjY2Pe5NMSQhCLxc7lewWDQRBCIBQKoVQqUVJSkhK+7rPxwvM8lpeXQQg5EwtjMeCv/3Vgb+8s7+v7+Px+CAWCu1VvsCywswP8N//NmdhGodwRKohRKJSMY7fbsbGxgeHh4awGpb969QrPnz/PmQl9oZBIJHB0dASbzQaWZaHT6c5NHL+EEIKZmRkYjca8mUheRzgcxtraWt4HTVPuhsfjwcbGBgDAbDantfteKBTC7OwsWltbUVlZmbbnvQl+vx/z8/MYHR0tyDKwRCKBmZkZjI+P3/qxp6enODg4QFdXF4CzY+Dw8BBP7pljs7Ozg+Pj47S6ANPhEltYWEB1dXVGulbelHg8DofDAbvdfi4IX6fT5Uw32Lm5OTQ3N+dcEw7K/eA4Dg6HA1arFeFwGEKhEOFwGGNjYzkZw0EIQTAYPCd8fVnmmBS+5HJ5Rp1tyY6bg4ODkE5NnQlXCgWgViMYDILn+buV5xMC7O6eiWu//dvAA0awUAoPmiFGoVAyyv7+PqxWa04EpqvVapycnBRUu/hsEY1GcXh4iKOjIwBnuRtPnz690cKEYRgMDg5iamoKAoEg7wNoaYbY46a8vBwjIyMIhULY2NjA+/fv0djYiKqqqnuJ7w6HA2traxgYGMjKokulUqGnpwdTU1MYGxvLiusmkxQVFYHn+Qvh+DdBLpcjFAql/v82gfrXUV9fD7lcjjdv3qTNTX3fLDGfz4dYLPbgYhjHcXC5XLDb7fB6vSgqKoJOp0NLSwsUCkVObmzV1dVhd3cX3d3d2R4KJY0IhUIYDAbo9XosLS2lIiAsFkvKBV9aWvrgxyTLsudEL7/fD47jzpU5lpeXo66uDmKxOCvfmWTA/tTUFDo7OqD9iZ8A/t2/Q5jjwIpEKLkiZP+rHB0BUinw9/4eFcMo94YKYhQKJWNsbGzA5/NhZGQkJ7IVKisrYbfbqSB2R0KhEGw2G+x2O0QiEYxGI4aGhu60UBYIBBgeHk51eUp3oPRDQrtMUoAzkaS3txfxeBzb29v43ve+h+rqatTV1d3K7UMIwcbGBk5PT/Hs2bOM5oV9DbVajc7OzpQolu1NjXSj0+ngcDhgMBhu9bji4mIkEonU/7vd7rRla+l0OkilUkxNTaG3t/feHT/v23FyZWXlQfIRCSE4OTmB3W4/F4RfXV2dkSD8TFBeXo6VlRUQQvJivJSbEw6HYbFYUF9fn/o+EELgdruxt7eH09NTqNVqmEwmlJeXp+3zT5Y5JgPt/X4/QqFQqswxWd5oMpmgVCqzer24CpVKhefPn2Nubg4nP/7jqDo4gPC730WJyYRbv0s8D9hsgEgE/NIvAXnQ5IOS++Tet4ZCoeQ9yTwUAOjv78+ZiaFGo8HHjx+zPYy8wufzwWazwel0QiaTwWg0pm1hLBQKMTIygomJCQgEAmi12jSM+OGhDjHK5xQXF6O1tRVmsxkHBwd48+YNNBoNmpubv+qgZFkWc3NzUKvVGBoayolzp0ajgdlsxtTUFEZHR3NicyNdVFVVYWNj49aC2JckEom0xgGoVCqMjY1henoaTU1N93bR3tUldnR0lCqrSjeEEAQCgVQQPsuyKCsrS7nA8lF8ZRgGOp0Odrv9wUucKZnj6OgI6+vr6O/vP9cQKinaVlRUgBCC09NTWK1WrKysoKSkBCaTCRUVFTc6j/M8j1AodE74isViAM5E7aTwpdfrc9YheR0ikQhDQ0OwWCx4NzaGv15VBeaP/xjweoGqqnNB+5dCCBAInIXoV1aelV7+2I89yNgphQ8VxCgUSlrheR7z8/MoKSmB2WzO9nDOIRAIIBAIwLJsTu6i5QLJXXqbzQaPx4OSkhIYjUa0trZmZCEsFAoxOjqKiYkJCIXCvHTvUUGMchkCgSDVZcvpdGJubg7FxcVoaWm5NGMoGAzCYrGgvb0dOp0uCyO+Gr1eD5ZlMTMzg+Hh4bxbjF2FUqlEIBC4k6OHYRjwPI9oNHq37mhfQSwWY3x8HHNzcwgGg/e6nkokEigUilu5xHiex8ePH+8dyP85kUgkFYQfiUSgUqmg0+kwMDCQ1XzRdFJXV4fl5WUqiBUAPM/j/fv3iMfjeP78+bUiLcMwKCsrQ9n3y/d8Ph+sVivW1tYgl8thMpmg0+nA83yqk6Pf70cgEEiVOSoUCqhUKmg0GtTX12etzDFTeL1eRCIRvPgrfwWv19Yw2N8Pxb/6V2fB+MBZl0iZDJBIAIYBOA4Ih4FQ6EwMk8uBH/7hM2dYdXV2/xhKQUFD9SkUStrgOA7T09MwmUw/aLWcY6yvr0OlUt3bEVBI8DwPl8sFm80Gn8+H8vJyGI1GlJWVPdhkLB6PY2JiAj09PfcuEXpoeJ7H5OTkncK5KY8Ln8+HjY0NxGIxNDU1QafTgWEYHB8f4+PHjxgcHMxYt8p0sLe3B6fTmVPO3/uyuLiI+vp6lN4yy2ZhYQFmsxkejwccx6G+vj4j4yOEYG1tDbFYDD09PXfemIhEIpibm7uxwLW5uQmBQJDqpHkX4vE4nE4n7HY7AoEApFIpdDod9Hp9wXYvBYA3b95gaGioYES+x0gkEoHFYkFNTc2tO2ITQhCNRlNOL6fTiePjYwSDQYjFYlRWVqK6uhpqtTpnyxzTTSAQSHV6F4vFiEQimJ2dRYPBANP2NvCd7wDv35+JX7HYWWmkUHgmkJWWnpVG/tiPAV1dZ2IZhZJGqCBGoVDSQjwex/T0NJqbm3N6Z9Tn82F7e/tBMlFyGY7jYLfbYbPZEIlEUFFRAaPRmNXuWLFYDJOTk3j69GledekihODt27dpdVJQCptoNIpPnz7B5XJBKBRCLBZjYGAgL8rEtra24Pf70dPTUxCimMPhgMfjQVtb260et76+DrVaDZvNhubm5nOlVJlgf38fBwcHd85tBM7Ev2QZ13UkNyhevnx5q8+Y4zi43W7Y7Xacnp5CJBJBp9NBp9NBqVQWxPFyE/b29sBx3L3EREr2sNvt+PDhA/r6+q7tgMjz/IVujl+WOX7ezZFhGITDYdhsNhwfH6eyWA0GQ8E1LfmccDiM6elpjIyMnBPCeZ7H8vIyCCHo7u6GIBI5c4s5nQDLnoXmV1cDJtOZOEahZAgqiFEolHsTiUQwPT2Nrq6unC95I4Tg1atXePnyZbaH8uAkEgkcHR3BZrOBZVno9XoYjcaccqREo1FMTk5iYGAg4wvMdPLmzRsqiFFuBcuymJ6eBsuy4DgOBoMBjY2NeeEq+fjxIxKJBJ48eZLtodwbnufx9u1bPL9lOLPVakU8HofVasXz588fROxxu91YWVm5s5Pwpi6xxcVFVFVVfbV0N5mblAzCB84y53Q63YM6jHMNlmUxMTGBFy9eZHsolFtACMHq6irC4TCePn2acm4lEokL3Rx5nk+VOSZFL5VKBbFYfOPXS3brPjw8BMMwqKqqgtFozItrwE2JxWKYmJi4dk53cHCAnZ0dDA4OFrRzlJK7FL5Hk0KhZJRAIACLxYL+/v5rd9JyBYZhIJVKEYlEHsWFNznhOjo6AgAYDAY8ffr0q+He2UIikWBkZARTU1MYGhrKKbGOQkkXyfNmZ2cntFotCCE4PDzE9PQ0FAoFWlpacvrYb21txcrKCj5+/IjW1tZsD+deCAQCiEQixGKxWy1m5XI5PB4PhELhgwk/Go0GAwMDmJmZQXd39603oKRS6VezxAKBAMLh8KViGCEEwWAwlQPGsizUajX0ej3MZnNeOBwfApFIBKlUikAgkFcbO4+ZcDiMqakplJaWQq1WY2lpCaFQCMDZ55kUvGpqaqBUKtNyrEskEjQ0NKChoQHxeByHh4ewWCzgeR4GgwFGozFn52o3gWXZVLfc674H1dXVKCkpwdTUVOqaSKE8JNQhRqFQ7szJyQmWlpYwNDSUkVDhTLG7uwvgLPy2EAmFQrDZbLDb7XlryQ+FQqkA73w4tqhDjHJTDg8P8enTJwwODl56bHs8HmxsbAAAzGZzzrpuCSF49+4dVCoVGhsbsz2ce7Gzs5NqgnBTYrEYXr9+DaPReOtyy/sSj8cxMzODuro6mEymWz32ay6xiYkJdHV1pRawkUgEDocDdrsdkUgESqUSer0eWq22oJws6SaZnVYILspCgud5BAKBc26vk5MTOJ1O1NfXQ6fTpUodZTJZVlyOLMum3PyJRAJ6vR4mkykv5kJJeJ7HxMQEWlpabixwJRIJzM3NoaysDGaz+dE6TCkPDxXEKBTKnXA4HPj48SNGRkbyblIciUSwtLSE4eHhbA8lLRBC4Pf7YbPZ4HQ6IZPJYDQaodfr83rHPumi+TJ3IhehghjlayTD0ZPlOF/7boZCIWxsbMDv96OxsRFVVVU5t0AghMBisUCv1+dsI5WbEI1Gsbi4iJGRkRs/hhCC//Af/gNevHhx486N6STZ0VmhUKC1tfVWx8ZVWWIOhwMHBwcwGAyw2+3w+/2QSCTQ6/XQ6XR5tSDPNoQQfPPNN7fOYaOkh3g8fk70CgQCqTJHpVIJlUoFpVKJo6MjRKNR9PX15WS4fTLv1Wq1IhqNQqvVwmQy5bTzkBCC6elp1NTUoKqq6taP3djYwMnJCQYGBnLyM6EUHvQoo1Aot+bg4AAHBwcYGxvLy4uVVCpFNBoFISRvJ6qEEJycnMBqteLk5AQlJSUwGo1obW29cxeyXEOpVKKvrw9TU1MYGxu7VTkThZJLJBIJzM7OQq/Xo729/UbnHblcjt7eXsTjcWxvb+N73/seqqurUVdXlzPnXYZhMDAwgOnpaYhEolsvfnIFiUSCeDwOnudvfP5kGAaRSARlZWUZHt3lCAQC9Pf3Y319HXNzc+jr67vx2FtbWzE3N4eKigpwHAePxwO73Y6FhQXU19cjFAqhqanpUQXhpxuGYVBRUQGn0/nVLDbK3SCEIBKJwO/3w+fzwe/3IxwOAzgrc0xme9XW1l4oc4xGo7BYLKiqqkJXV1fOHudCoRBVVVWoqqoCz/NwOp1YX19HMBiERqOByWRCSUlJzoyfEIL5+XlUVlbe6XrAMAxaWlrgdDrx+vXrvIljoeQ31CFGoVBuxebmJk5OTtDf35/Xwsvi4iLq6+tRWlqa7aHcGJ7n4XK5YLVa4ff7UV5eDqPRWPDhxV6vF4uLixgbG8tZNyJ1iFGuwu/3Y25uDk+ePLmXk4jn+VT4sEajQVNTU844J3mex+TkJJqbm/N28b+6ugqtVnvj8h5CCH77t38bP/MzP5P1a6HNZsP29jaGh4e/eo4khMDr9WJqagoCgQAymQwajQbxeBwymQxms/mBRl34BINBrK6uYmhoKNtDyWsuK3OMx+MAzjY4Pw+1v0mZo8vlwsrKCp4+fZpXc8DPIYTA7XbDarXC6/WirKwMJpMp6/PBlZUViMXitJxHIpEIZmdnUV9fj+rq6jSMjkK5HCqIUSiUG5HsvsNxXE7vpt2Uo6Mj+P1+tLS0ZHso1/KlXb6iogImk+nR7Zh5PB6srKxgbGwsJ7PQqCBGuQybzYatra20ds8ihMDpdOLTp08oLi5GS0sLSkpK0vLc94HjOExMTKC9vR0ajSbbw7k1p6en2N/fR3d3943u7/P58N3vfhc/8iM/khMNEE5OTvDu3bsL3dySQfgOhwMOhwOJRAKlpaUoLS3F3t4eXrx4gUQigTdv3uDly5dZF/cKjdevX2N0dDRnXJ25zFVljgKB4Fw3R6VSeSfHOCEEHz9+hM/nQ19fX07OJe7ClxUDpaWlMBqNqKioeNC5+vr6OuLxeFpz83iex9LSEhiGQVdXFz0/UTICFcQoFMpXIYRgYWEh1f2sEEh2v8lFESMej+P4+Bg2mw0sy0Kv18NoNObEoiubuFwufPjwIScXF1QQo3wOIQTv379HLBbD06dPMzaJ9/l82NjYQDQaTbmzsrlZkUgkMDExge7ubqjV6qyN4y4QQvDq1Su8fPnyRvff3t7G0dERzGZzznRFC4fDmJmZQXNzc2ozJRwOQ6FQpILwPxcSkllih4eH0Ol0qKyszOLoC5OdnR0wDFOwTXxuCyEE4XD4nPCVLHMsKipKBdqrVCooFIq05aDGYrFU3mFjY2Peb+peBSEEPp8PVqsVbrcbCoUCJpMJWq02o2LS7u4u3G43+vr6MvLeHhwcYHd3FwMDAznjjKYUDlQQo1Ao18JxHGZnZ1FZWVlwE7rXr19jbGwsJ4Lno9EobDYbjo6OwDAMDAYDqqqq8rrldiZwOBzY2NjA6OhoTnxuSaggRkkSj8cxOzsLg8GAhoaGB3nNaDSKT58+we12o66uDjU1NVnbSY/FYpicnERfX1/eOVktFgva2tqgUCi+et/Z2VmUlZVBKBSivr7+AUZ3NYlEAk6nEw6HAycnJ3A4HKivr0dXV9e1QfiRSASTk5MQi8UYHx9/wBE/HhKJBKampvD8+fNsD+VB4TjuQpljIpEAAMhksnPCl1QqzahA5Xa7sby8jJ6enqxl/mWLQCAAq9UKp9MJiUQCk8mU9oZLh4eHODg4wNDQUEY/R5/Ph/n5+XvHD1AoX0IFMQqFciXJiVxTUxMMBkO2h5N21tbWoNFospZ5EwqFYLVa4XA4UFRUBKPRiMrKyoKx8WeKo6MjbG9vY3R0NGfs81QQowA/mLB3d3ejvLz8wV+f4zjs7u5if38flZWVaGxszEruXiQSwdTUFIaGhvLK2Xp4eIhgMHij/JtXr16hs7MTR0dH6OzsfIDR/QCe5+F2u+FwOODxeCAUCqHVaqHX61Mi5OLiIoqLi9HR0XHtIvX3f//30dfXV3AbXrnE9PQ0njx5klffhZsSi8UulDkSQiAQCFLdHJO3hz4XJTsWejwe9Pf352wG6UMRDodhtVpht9tTc06DwXAvx30y5H9sbOxB5mOJRAIWiwUajQbNzc0F6/SjPCxUEKNQKJcSjUYxNTWFJ0+e5GUezE3weDyw2Wzo6up6kNcjhMDv98NqtcLlckEmk8FkMkGn0+WU2ykfsNls2N/fx/DwcE6IYlQQoyRLOgYHB7Pu7CSE4OjoCFtbW1AoFDCbzTdyPaWTUCiEmZkZjIyM5E2Jy01L6WOxGBYWFtDb24ulpaWMh6Yng/AdDgecTicIISgvL4der0dZWdmV58BPnz6lmuBcdo1xuVzY2tpCIpGg568MYrfb4fF40N7enu2h3IlkmWOyk6Pf70ckEgEAFBcXnxO9lEplTlyT4/E4LBYLKioqqHByCZ9XJQgEglQny9uIhqenp1haWsL4+PiDxlgkhc7T01P09/fnXIQGJf+gRxCFQrlAMBjE7Ows+vr6ciKsOVOUlZXh/fv3GX0NQkhKeDs5OUFJSQlMJhPa29vpBO0eGI1GcBwHi8WCwcFB+l5SsgYhBMvLy+A4DuPj4zmxGGQYJrXA8Xg8eP/+PXieh9lsfrANDrlcjv7+fkxPT2N0dPROIdgPjUgkAsMwYFn22kWW2+2GRqOBWCxGLBbLyFiSQfh2ux2JRAIlJSWp/KObLgCbm5txdHSEt2/fYnh4+NxnkMy5Gx0dxdraGlwuFy1DyhA6nQ4fPnxAW1tbTl+rvixz9Pl8YFkWwNn3WaVSobS0FNXV1Rkvc7wPHo8HS0tLWXPq5gMSiQSNjY1obGxELBbD4eEhLBYLeJ6HwWCA0Wi8dmMnEAikun8/tCDFMAxaWlrgdDrx5s2bvCzPp+QW1CFGoVDOcXp6isXFRQwPD1+bPVIoTE5Opt1Kz/M8nE4nbDYbAoFAqh22Wq3O2QlkvrKzswO3243+/v6svrfUIfY4icVimJ2dhclkyvmSs1AohI2NDfj9fjQ0NMBoND7IdybpIsjVDrFfsrm5CalUCqPReOV9lpeXUV1dDbVajdevX6clHyoWi6UEsFAoBLlcDr1eD51Od28x0ev1YmFhAf39/amF4+7uLuLxOMxmMyKRCObm5ug5LIMsLy/DYDDkhOj4tTLHz7s55lOZISEEnz59gsvlwsDAQF6NPVdIJBI4Ojo619TJZDKdWw8kS+JzYZ0QiUQwOzuLhoYGmEymrI6Fkr9QQYxCoaRwOp348OHDhZ3kQmZrawtisfjeF1KO41KdIaPRKLRaLYxGI921egC2trbg8/nQ29ubNVGMCmKPj+TmQb4FNcfj8VSHxOrqatTV1WV8hz/ZITZXmphcRygUwurqKgYHB6+8z+vXr1NuwDdv3mB8fPzW5x6WZVNB+F6vF2KxGDqdDnq9PiNZU5FIBDMzM2hra0N5eTlev36Nly9fphyNyY6TuSDYFCJ+vx/r6+sYGBh4kNcjhCAUCp0Tvj4vc0yKXslujrngbL0P8Xgcc3NzKCsrQ0tLC918TANfzmt1Oh20Wi2Wl5fR398PpVKZ7SECONuEXlpaAsMw6OrqyvtjmfLw0JJJCoUCALBardjb28uK/TmbVFZWYm1t7U6CWDwex9HREQ4PD1M7aU+ePMn6jtljo7GxEevr61heXkZXVxedCFMyzv7+fup8mW+bB8XFxWhtbYXZbMbBwQHevHmD8vJyNDc3Zyzrq6KiAmazGVNTUznVDOMy5HI5wuEwCCGXnkt4ngeA1N8glUoRiUS+et7neR4ejycVhC8QCFBRUYG6ujqUlJRk/LwllUoxPj6O2dlZvH//Hq2trec+h9bWVszNzVFBLEOoVCqEw2FwHJdWUZjjuHOil9/vv1DmqFarUVNTA4lEUpDXx+TmRFdXV8Fm3mYDoVAIo9EIo9EInudxeHiIP/uzP0NZWRn29/dhMplyIlZFIBCgt7cX+/v7ePv2bU7keFLyi8ez6qVQKFeyvb0Nl8uV8wuVTCCXyxEKhW58/8+DSJM5PX19fXm3KC40WlpasLa2htXV1Qfv+JbkqgU0pXDgeR7Ly8sAkDN5YXdFIBCgtrYWNTU1cDqdmJ+fR3FxMcxmM0pLS9P+enq9HizLYmZmBsPDwzn9XSkrK8Pp6emlzr/T01Oo1erU/8vlcgSDwQuCGCEEPp8PdrsdLpcLPM+jvLwcOp0ObW1tWTl2RCIRuru78Z/+03+Cy+WCwWBIfQ5SqRQKhYJmiWUQo9EIm82GmpqaWz82Go2eE72CwSAIIRAKhalujgaDAS0tLXlRmpwOCCHY2tqC3W7Py82JfGN3dxc//MM/jIqKilRDDp/Ph/Ly8pyIBampqUFpaSkmJyfx5MkTeh6j3BhaMkmhPGIIIfjw4QNisRh6enpyeoGSSebm5tDa2nplF7ZgMAibzQa73Y7i4mIYjUZUVlY+mklnPrGysgKRSIS2trYHfd23b98+SkH5MRGNRjE7O5sSkQoRn8+HjY0NRKNRNDc3Q6fTpf26sLe3B6fTmfXcv+twuVyw2+2XiusbGxtQKpUwGAwAztyCPM+jrq4OoVAIdrsdDocDsVgMpaWlqTKjXHFeT09Po7W1FR6PB3a7HYODg6mx0SyxzBKPxzE7O4vx8fFLf08IQTAYPCd8RaNRAIBYLD7XzbEQyhzvQyKRwPz8PEpKStDa2pqz55JCgBCCmZkZmEymC9mKhBCcnJzAarXi5OQEpaWlMJlM0Gg0WftMEokELBYLNBoN7TBKuRFUEKNQHimEECwuLkIqlT76yYTVakUsFkNjYyOAH+zs22w2uFwuyOVyGI1G6HS6nM+/eewQQrC0tASZTAaz2fxgrzsxMYHh4WF6fBQoJycnePfuHXp7e8+5gwqVaDSKzc1NuFwu1NXVoaamJq2L783NTQQCgZzdiCGE4PXr13jx4sWF333eiCUWi+HTp0/Y2dmBSqWCXC6HTqeDTqfLyZIdt9uN3d1d9Pf3AwAcDgc+fPiAoaGhVLkszRLLLFNTU+js7ATLsqlOjn6/HxzHgWGYVJlj8laoZY73IdkkorOzE1qtNtvDKWgIIVhYWEB5eflXG8ck584HBwfweDxQKBSorq5GRUXFg4u3hBCsr6/D6/Wiv78/ZzYkKLkJFcQolEcIz/OYnZ2FTqdDfX19toeTdeLxOCwWC1paWmCz2VK7XEajERUVFXQymmckJ3ClpaUpkTPTTE1Nob+/n7oGC5Dd3V1YrVYMDg4+upIcjuOwu7uL/f19VFZWorGxMW2d2z5+/AiWZbNW4vw1pqen0d3dfS5XjWVZ/Omf/imMRiO8Xi+Ki4uhVqvhdrtz3lWVFPmGhobOiXV+vx9zc3N4+vQpSktLqUssTRBCLnRzTLq/4vE4GhsbzwlfdMH+dQgh2NnZweHhIQYGBnJSdC40VlZWUFxcjJaWlls/1u/3w2q1wuVyQSqVwmQyPfjGssPhwNraGvr6+miTK8qVUEGMQnlksCyLqakpNDQ0oKqqKtvDySo8z8PpdMJqtWJ1dRW9vb05kYNAuT+EEFgsFmi12q/uaqaDmZkZ9PT0PDrBpJDheR7v3r2DSCTCkydPHvU5gRCCo6MjbG1tQaFQwGw2X1lifhvus9jKNPv7+0gkEigtLYXdbofH4wHLsgiFQhgfH0dpaSkYhgEhBG/evMHz58+zPeRr2d/fRzgcRmtr64XfxWIxTE9Po7m5GQaDgbrEbgHP8xe6OX5e5vh5N8dk99BXr17h5cuXj/qccltYlsX8/DwUCgXa29vpe/cAJMvnu7q67v1coVAIVqsVdrsdYrE4FT3yEEJwOByGxWJBQ0PDvTvKUwoTKohRKI+IaDSK6elpdHR0PNqJ7pdtpLVaLYxGI/b29lBVVYXy8vJsD5GSJpK5F1VVVaiurs7oa1ksFnR2dmasSx/lYYlGo5iZmUF9fX3Gj518w+Px4NOnT+B5Hmaz+V5d3ZKl+6WlpWhoaEjjKO8+Hr/fD7vdjuPjYxwcHKC7uxt6vR4ajQY2mw0sy15wVr9+/TqnBTGO4/Dq1Su8ePHiSncGx3GwWCypgGzqEjtPssTx89vnZY6fC19isfhawebdu3eoqam5tGkD5SI+nw/z8/Po6OiATqfL9nAeBbu7u3C73ejr60u7+BiJRGCz2XB8fAyBQACj0YiqqqqMOuyTG1xCoRBPnjx51Pl7lItQQYxCeSSEQiHMzMykyiIeE/F4HEdHR7DZbOB5Hnq9Hkaj8VxXMKfTCZfLhfb29iyOlJJueJ7H9PQ0amtrM+qInJubQ3t7+4VOc5T8w+PxYGlpCX19fTnRUj5XCYVC+PTpE3w+HxoaGmA0Gu+0cEq6OfV6fVaaFYTD4VQQfjQaRUlJSSoIf2pqCmNjYykRaWFhAU1NTRdKb16/fo1nz57lrGtlbW0NKpXqq+4IQghWV1cRj8cBIJX/81gghFzo5hgKhVLdHFUqVUr4UiqVd3a3eL1ebG1toa+vL81/QeGxu7uLg4MDDAwM0A2nB+Lw8BD7+/sP0g04Fovh8PAQh4eHAACDwQCj0Zgxt/3+/j729vYwODhIS24pKWjBOoXyCEgGkA4NDaUs+4VOJBLB4eEhjo6OIBAIYDAY0N/ff+VFVqPR4OPHjw88SkqmEQgEGB4exuTkJIRCIfR6fcZeh+f5jDw35eHY3t7G0dERxsfH05aVVajI5XL09PQgHo9je3sb3/ve92AymVBfX38roYBhGPT392N6ehoikSjjpfzxeBwOhwN2ux3BYBAymQx6vR49PT0XFkharRYulyt13ggEAlAqlReeUyqVIhKJ5KQgHolE4Ha7b9R5l2EYdHZ2Ym9vD7u7u1hdXcXLly8fYJQPC8/zF7o5xmIxAIBEIkk5vfR6PRQKRdpFgdLSUgQCAfA8T50qV8BxHBYWFiCRSHJabC40nE4ntre3MTo6+iDvuVgsRn19Perr65FIJHB0dIT5+XmwLJsSx9IphNbU1KCkpASTk5Po6uq6l8OZUjhQQYxCKXBcLhdWV1cxOjpa8LshwWAQVqsVDocDYrEYVVVVGBkZudHiTCAQQCAQgGVZGm5bYAgEAoyMjGBiYgICgSAjXamoIJbf8DyPhYUFiMVijI2N0cXXLSguLkZrayvMZjOsVivevn2LsrIyNDc333gh87lwLRKJ0loWxXEcXC4X7HY7vF4vioqKoNPp0NLS8lWxw2AwYGtrC3q9HizLQigUXnp/uVyOUCiUk4LYysoKOjs7b3VM19bWQi6X4y/+4i9wcHCQt2XDiUTinOgVCARSZY4KhQIqlQoajQb19fVfLXNMNwaDAYeHhzTT6BL8fj/m5+fR2tqKysrKbA/n0XB6eoq1tTWMj49npWN2UVERampqUFNTA5ZlcXx8jKWlJcRiMej1ephMprRs6peWluLZs2ewWCw4OTlBU1PTjb77iQTw/j2wtQXs7gI+HyAUAuXlQEMD0NICNDYCdPqQf9CSSQqlgDk8PMT29vaNRaF8I9ni2Wq1wu12Qy6Xw2g0Qq/X32nXdWNjA0qlEgaDIQOjpWQblmUxMTGBjo6OtO8KLi0toa6ujpbY5SGRSASzs7NobGyE0WjM9nDyHkIIXC4XNjY2UFxcDLPZfOMy/XR8RwkhODk5gd1uh9vtBsMwqKiogF6vTwXh3+a5ktlbLpfrSqfV3t4egDMhKZc4OTnB5uYmBgcH7/R4l8uFP/mTP8Ff/at/NWfzrr4sc/T5fAiFQgAAkUh0rpPjfcoc000sFsPc3BzGxsayPZScYn9/H7u7uxgYGMhJgblQCQQCsFgsGBsby7nmQDzPw263w2q1IhwOQ6vVwmQy3btrJCEE6+vr8Pl86Ovru/Lc4PUCf/RHwB/8wZkQFo8DhFwUvuRyoLsb+KmfAv6r/wrIkVMN5QZQQYxCKVB2dnbgcDgwODhYUJZ8Qgg8Hg+sVitOT09RWloKk8kEjUZz791dn8+H7e1t9Pb2pmm0lFwjkUhgYmICXV1daV3grayspDqUUvIHl8uFlZUV9Pf305bsGcDv92N9fR3RaBTNzc3Q6XRfPU8nv6M9PT03EtIIIQgEArDb7XA6nWBZFmVlZdDr9SgvL7+30yGZG2a1WlFRUXGpw9TlcsHpdOZUBmWy++V9s5csFgs8Hg86OzuzKhh/Webo8/lSWWcSieRCN8d8cHlOTEygr6+v4N37N4HjOCwuLqKoqIiGnj8wkUgEU1NTGB4eznkRkud5uFwuWK1W+P1+aDQamEymW292fI7D4cDa2hr6+/vPlcQTArx5A/zarwGbm0BxMVBRAUilF8UwngcCAcDjOfvdyAjw9/8+8EX/FUqOQgUxCqXAIITg48ePiEQi6O3tzYtJ4dfgeR5OpxNWqxXBYDDVBes+F8DLSLoBCjEzhfID4vE4JiYm0Nvbm7YGE6urq6isrKRdSvMEQgi2trZSmwaZ7G5FOevaubm5CZfLhdraWtTW1l674I3FYpiYmLhSqIxEIqkg/Egkksp70mq1af8s7XY7Tk9P4Xa7MTIycqnAFolE8P79ewwMDKT1te9DcsF4X5Eu6aCUyWRQKpVoaWnJ6LziyzJHv98PnudTZY5fdnPMZ2w2G0KhEMxmc7aHklUCgQDm5ubQ0tJCHfoPTHI+1NfXl3ebQl9ukKvVaphMJpSXl9/6HBUOh2GxWFJOcZ4H/uW/BP71vwZiMcBkAm56aQmFgMNDQK8H/uE/BOiSIvehghiFUkAQQrC0tITi4mK0tbXltRiWzA84PDxENBpNWaQvCzROJzMzM+jq6qLdjAqcaDSKycnJtDmD1tbWoNPpaEBrHpAMa5bJZGhvb8/r82S+wXEcdnd3sb+/D71ej6ampiubFyRdC0NDQygqKoLT6YTdbkcgEIBUKoVer4dOp8v4uZrjOLx9+xYA8OLFi0vvk3RjPX/+PKNjuSkcx6VKPdORBbS4uAiTyQSXy4VQKISnT5/ey8FDCEEkErnQzRH4QZnj590cs5Fn9BDwPI/Xr18/6k24g4MD7OzsoL+//9E0fcoVkiXqT548ydmS6JtCCIHX68XBwQE8Hg9KSkpgNBqh1WpvfI3neR7v3r2DQCDE69dP8Fu/JYBSeeYKuy08D+zvA0ol8Cu/AuTIpYFyBVQQo1AKBJ7nYbFYoNFo0NjYmO3h3Il4PJ5qv8zzPCorK1FVVfWgFu69vT0QQlBXV/dgr0nJDskF9+DgIBQKxb2e6+PHjygvL89IYD8lfYTDYczOzsJsNlMnQhYhhODo6AhbW1tQKBQwm83nvoMcx8HtdmN/fx+rq6uor69HVVUVdDodlErlg4uY3/3ud6FUKq91gL1+/TpnBLGPHz9CJpOhpqYmLc8XiUQwNzeHZ8+ewWq1Ynd3F0NDQ1/txMrzPAKBwDnhK1nmKJVKU06vkpISyGSyRylOLywsoKGhIW1u5XyB4zgsLS1BIBCgq6uLlkg+MDzPY3JyMlXKXmj4fD7YbDa4XC7IZLJb5Qv/9m/b8b/+rwqUl0uh1d5djCfkLHNMpwN++7cBGlGau9C4NwqlAGBZFtPT06irq8u7UOhIJAKbzYbj42MIBAIYDAb09/dnrRRCr9fj3bt3VBB7BEilUgwNDWFmZube2Rm0y2Tu43Q6U2VtmXaaUq6HYRhUVVWhqqoKJycnWFlZQSgUglKpTAkmGo0GDQ0NMJvNWFhYQHV1ddauCzd9XUJI1kWdaDQKh8ORVnFOKpVCoVDA7XbDZDJBJpPh7du3qc2EeDx+oZtjssxRqVRCpVJBq9WisbEx78sc001dXR12d3fR09OT7aE8GMFgEHNzc2hqasq7OWshQAjB7Ows6urqClIMA4CSkpJUk6NkB/pPnz5BLBbDZDKhsrLyUuepywX823+rR1ERB4HgBPF4yVeF/6tgGKCmBtjeBn71V4F//s8BqvvmJlQQo1DynFgshunpabS1teWNOyUQCMBms8HhcEAsFsNoNOZMJ0yJRIJoNJoTCxtK5pHL5RgcHMT09DRGRkbuXH7FMAyo4To3IYTg06dPcLvdeP78eU6cZx47ySB8h8MBh8MBlmVRUlKCcDgMnudTC+XkObinpwdTU1MYGxvLWt5bNBq99vfJa0e2y+3fv3+Pjo6OtF+/WlpaMD09jfb2dvh8PojFYvz+7/8+dDodSktLU26v2tragi5zTDdqtRpLS0vgef5RuKRsNhs2NzcxMDBASySzACEEi4uL0Ol0j0aMVCgUaG1tRWtrK8LhMGw2GyYmJiASiWA0GmEwGFLXlX//789cXfX1QggE5fB6vRCLi79/rN7+nCoUApWVwKtXwMzMWdg+Jfegs0IKJY8Jh8OYnp5Gb29vTne3I4TA5/PBarXC7XZDLpfDZDLBbDbn5ASwtLQUPp/v0ZUwPFYUCgX6+/sxNTWF0dHRO3X8og6x3ITjOMzNzUGlUmFkZISK3FkkEonA4XDAbrcjEolAqVRCr9djYGDg3A58IpHA9vY2vve978FkMqG+vh5qtRodHR0pUeyhxRaWZQFc7wCTy+UIhUJZFcS8Xi9Ylr1XliHHcalujj6fD36/H4lEAgBS13Gj0Qij0YiBgQHMzs6iqqoqbeWZjw2GYVBZWYnj42NUVVVlezgZg+d5LC0tgRCC58+f5+Tc7zGwuroKuVyO+kfa/lAmk6G5uRnNzc2IRqM4PDzEzMwMAECtNuIP/7AGcrkAZ/tmApSVqREIBHF66kVpaQkY5vbHrVIJOBzAd75DBbFchWaIUSh5is/nw/z8fFryjzIBIQRutxs2mw2np6coLS2FyWSCRqPJ+UXp8fExfD4fWlpasj0UygPi9XqxuLiIsbGxW1vkd3Z2UFRUBJPJlKHRUW5LKBTC7OwsWltbUVlZme3hPDoSiUQqCN/v90MikaSC8G9SnszzPKxWK7a3t1FeXo7m5mb4fD5sbW1hZGTkwRbUsVgM8/PzqU6WFVckLO/u7kIgEGRNGCKE4O3bt+jr67vR+/t5maPP50MgEAAhBAKBIFXmmLwlz4efZ4kl4Xkei4uLkEgktEnFHYlEIlhcXMTo6Gi2h5IRQqEQ5ubm0NDQQK+RWWRjYwPRaBRdXV3ZHkrOEY/H8R/+gwf/0/+kgkYTg1JZDIlEmtp8icWiCASCKC0tvZPL3O0++/dP/gTI8/4FBQl1iFEoeYjb7cbKysqd3SyZgud5OBwO2Gw2BINBlJeXo7a2Ft3d3Xk1Sa6oqMDm5iYVxB4ZpaWl6O7uxuTk5K1Ls2jJZG7hcDiwtraGgYGBnNwwKEQ4joPH44HdbsfJyQlEIhG0Wi2ampruFISfFJeqq6vhcrmwsLCAoqIilJeXY3Z2FkNDQw9yXfF4PNBoNKioqIDVar1SEFMoFHC5XBkfz1UcHR2htLT0nBhGCEE4HD6X7xUOhwEARUVFqU6O9fX1UCgUX3XefZ4llnShCQQC9PX1YWNjA7Ozs+jv76flkrdEKpWC53nE4/E75xXlKoeHh9jY2EB/fz/Nbswie3t78Pl86O/vz/ZQcpLi4mIEApWQSACdTopoNAa/3weeJxCLxZBKJVCr1fB6TyGXyyGR3M4JrFIBR0fA5iYwOJihP4JyZ6ggRqHkGUdHR9jc3MT4+HjWslQ+h2VZHB8fw2azIRaLQafToaWlJa8nPiKRCIQQcBxHJ/aPjLKyMnR2dqZEsZvuBNKSydyAEIL19XV4vV48e/aM5oVlkGSbe7vdDpfLBUIINBoNqqqq0NHRkTYHF8Mw0Gq10Gq18Pv92NjYgNPpxHe/+138pb/0lzLuFHO5XDCZTFCr1VheXr7yfnK5HHt7exkdy1UkEgksLS2hra0Nq6ur58ocZTJZqpOjyWSCVCq9l5DY2tp6wSUGAGazGYeHh5iYmMDQ0BANz78lNTU12N/fR1NTU7aHkhZ4nsfKygpYlsXz58/pXCqLHB0d4fDwkMYGfIX19bMgfIYRQCqVQiqVghCCWCwKvz8AnudQXFyMcDiCeDwOlUqFm+aKFRUBLHsWsE8FsdyDzhQplDxid3cXR0dHWclQ+Zx4PI7Dw0McHh6C53lUVlaiu7s762HC6aSiogJut7tgO/BQrkaj0aCtrS2VKXaT75pAIEgtQCnZgWVZzM3NQa1WP5h76DFBCEEwGEwF4ScSCZSWlkKv16O5uflBrkkqlQr9/f2IRqN4+/Ytfu/3fg+Dg4Ooq6vLmDDm9XrR2dkJhmEgk8kQCoUuDQOXSqWIRCIZGUOSWCx2oZsjIQQnJydQKpUghKQ+j0w5jS5ziSWpqqqCTCbDxMQE+vv7v79gpNyEqqoqvH37tiAEsXA4DIvFgrq6Opotl2VcLhe2trYwOjpKr4lfwe0+E64+h2EYSCRSiMUS8Dz//aZbcQQCEfj9ARiNVbiJKHYmtAGBQGbGTrkfVBCjUPKE9fV1BAKBrO3wRCIR2Gw2HB8fQyAQoKqq6kIYciGh1+thtVqpIPZI0Wq14DguJYp9bbFNSyazSzAYhMViQXt7O/3OppFoNJoKwg+Hw1AoFNDr9ejr68uqA0gikeCHf/iHsbq6it3dXezv70On06GxsTGt40q6PpPf/6qqKhwdHV0qWqTrHJAsc0wG2vv9/pTQVlxcnMr1qq+vh1KpRCKRwOTkJF6+fPlgc4OrXGLAWdfEkZERTE9Po6OjI2+6X2cboVAImUwGv9+f10Li8fExPn78iL6+vrz+OwoBr9eL1dVVjI+PU4cekCpL/vKWSCQQj8dxeqpHOCzByUkUl53KBQIBBAIBiouLUwLZbTtPUk0yN6GCGIWS4xBCsLy8DKFQiL6+vgcVwwKBAGw2GxwOB8RiMYxGI0ZGRh5FGZJarcbKykq2h0HJIpWVleB5HtPT0xgeHr5WFKMlk9kjuQAbHBy81LlDuTnJIHyHwwGfzweJRAKdTofOzs4bBbU/NB0dHeA4DmKxGEqlEjMzM5DL5TCbzWkp2/d6vee6Dev1ekxPT1/r4rmuE+XncByHQCBwrptjspulXC6HSqVCaWkpqqurry1zXF1dRUdHx4PODa5ziSV//+zZM8zMzCAYDD7ajna3pb6+Hru7u3kZek4Iwfv37xGNRmmJZA4QCASwsLBwq+iHfIDjuEtFrc+FreQteS5OblQkxawvb3K5HGq1GjU1CmxvF0GtlqX9fErI2S2P02QKmsL5hlAoBQjP85ibm0NZWdmD2OiTmTBWqxUejwcKhQJGoxFms/nRtchmGAZFRUWIxWI0C+URU1VVBY7jvhriTQWxh4cQgo8fP8Lv99MF2B3heR5utxsOhwMejwdCoRBarRYNDQ1QqVR5UWLz5MkTLC4uoqioCM+fP8fJyQlWV1fB8zyam5uvDMG/CS6X69zjk/mSLMteusgUi8WIxWLnmt18Xubo8/kQDAZT3RyTbi+DwYCWlpZb54L6fD5Eo9GsuLCuc4kBZ+/V6OgolpaWsLy8jCdPnuTF8ZRNysvLsbKycmNRNVeIRCKwWCyorq7GkydPsj2cR0/y8xgeHs65+Wsyn/c6Ievzn3/puhUKhSguLkZRUdE5USvZDTd5KyoquvW6pasL+N73MuPiiscBkQhoaEj/c1PuDxXEKJQchWVZzMzMoLq6GtXV1Rl7HUII3G43bDYbTk9PoVarYTQaU5kpjxm9Xg+Hw5HR95+S+1RXV4PjOMzNzaG/v//S74VAIKAlkw9IIpGAxWJBRUUFBgcHH/256qYkNz0cDgecTicIISgvL0dlZSXa29vzcuODYRj09vbCYrFAJBKhpqYGw8PDCIfD2NjYwNraGhoaGmA0Gm99nLjdbtTW1p77mU6ng8PhQFVVVepnhBCEQiEkEgm8f/8+lTUDnJU5Jrs5NjY2QqFQpO19XllZQU9PT1qe67Z8zSUGnH02PT092NrawvT0NAYHB6lwfQ0Mw6SOL71en+3h3AiHw4HV1VX09fWhpKQk28N59MTjcUxPT6O/vz9jrt7LRK2rbizLXilqfXkrKSm5IGo95LW9uRkQCM7Eq3SnwQQCgEIBNDam93kp6YEKYhRKmiGEYOtkC5snm9g53UEwHgQDBiWSEjSWNcJcboapxHTtc8TjcUxNTaG1tTUjeTg8z8PhcMBqtSIUCkGj0aCurg7d3d10YfkZer0ea2trVBCjoK6uDhzHYWFhAU+fPr3wPWEYhjrEHohAIACLxYLOzk6aT3QDkkH4drsdiUQCJSUl0Ov1aGxsLJhSGoZh0N/fj+npaYhEolS4e09PDxKJBLa3t/G9730PRqMRDQ0NN/67E4nEOYcFx3GQy+V4//49Tk5OzpU5KhQKAEBRURHMZjMkEklGr6fHx8dQKpWp180GX3OJJUkKgW/evMHw8PA5Bx3lPHV1dVheXs55QYwQgrW1NYRCITx//rxgziX5DMuymJqaQnd391fz2wghSCQSV7qzvhS1vkQkEl1Zfvj5/4tEorxZVwwOAkYj4HSe/ZtOTk+Bn/5poKwsvc9LSQ/07EWhpIlIIoL/vP2f8Qcf/wBrrjWE42GAQWpnhGEYMGCgKFZgoGoAP9nyk3he+xwiwfmvYTgcxvT0NHp6elCWxjMny7I4Pj6GzWZDLBaDTqdDa2trWnJWChW5XI5QKJR35QuUzNDY2IiNjQ0sLS1dEI9pyeTDcHh4iE+fPmF4eDgnM61ygWg0CqfTCbvdnuqImAtB+JlGIBBgaGgIk5OTEIlEqc2koqIitLS0oLm5GVarFW/fvoVarYbZbL60MzIhBLFYDA6HA8FgEIuLi6kyR6FQCKVSiUgkgsrKygtljk6nEx6PJ+Mdl3mex4cPH74qRGWam7jEkuj1ekilUkxOTlI30TXIZLKUUHHbEtqHIhqNwmKxwGg0oqOjI9vDebQkRa14PJ76TAwGA05OTmC328+JWhzHnXsswzCXilrJPMbPfyYUCh/FHFgqBX7yJ4Ff/3WAZc9KHNOB3w9IJMCP/Vh6no+SfhhCazwolHuzZF/CP5n4J1h2LINhGGhkGiiKFRAw58siOJ6DP+aHJ+KBiBFhvGYcvzT6S6gtrQUA+P1+zM3NYWBgIC1CVSwWw+HhIY6OjsDzPCorK2E0GjM+WS8k5ubm0NLSQoVDSooPHz6AZdlzWSmnp6ewWq00PyVDJN0I4XAYT58+pWVXn8GybCoI3+v1QiwWQ6fTQa/XP8omAyzLYmJiAp2dnSgvL7/we0IIXC4XPn36BI7joNPpQAiB3+9PlTmKxWLE43GIxWK0trZeKHN89+4damtroVarzz13KBRKddjLJJubmxAIBGjIgUCaSCSC+fl5jI+P3+j+0WgUMzMzMJvNqKyszPDo8pPd3V3wPJ8Tn++XOJ1OvH//Hk+fPj3XcIJyN3iev5FLKx6PX9h0S2bdFhUV4eDgADqdDgaD4ULZ4WMSte7LyQnwMz8D7O8DdXX3fz6OA7a3gW99C/i1X6NdJnMVKohRKPeAEILfW/09/PrMr8Mf98OkMkEsutkOfDAexFHgCJWKSvzyi19Gh7IDy8vLGB4evpdgFYlEYLPZcHx8DIFAgKqqKlRVVaE43QXxjwSbzYZIJPIgTQ0o+cP79+8hFArR1tYG4Kwb3f7+fl52B8t1EokEZmdnU2V+jx2e5+HxeFJB+AKBABUVFdDr9SgpKaGLHpzFDkxMTKC3txcKhQKBQCDVydHv94PjODAMA4FAAJ/PB4Zh0NraipqampTwtbi4iMbGxktLj5xOJ1wuF9rb28/9nOd5TExMZNS5lfzbXr58mTOf9cLCAmpqar7qEkvCcVwqA5B+py/CsiwmJyfx/PnzbA8lBSEEHz58gN/vR19fX86617IBz/Nf7Xh4nah1mYB12e2yjSBCCBYXF6FWq2k31zTx3e8Cv/iLZzli90llIATY3QUqK4Hf+Z2zfym5CRXEKJR78Lsrv4tfnfpVCAVCVCoqbz055QmPfe8+JIwEP2P4Gfydv/J37iRcBQIBWK1WOJ1OSCQSVFVVobKykmY6pIF4PA6LxYKxsbFsD4WSQxBCsLS0BJlMBrPZDL/fj+3t7awFXBcqSddsV1fXjRfbhQYhBD6fD3a7HS6XCzzPo7y8HDqdDuXl5XkZhJ9ukmWOyU6OyX/39/dRXV2NioqKVEdHlUp14doYjUaxtbUFp9OJ2tpa1NTU4O3bt3j+/Pml13We5/HmzRu8ePHiwu9ev36dUSHj3bt3MBgMGckXvSu3dYkBZ5/Z+/fvwbIsenp6ckbcyxVmZ2fR3t6e1Yy4JLFYDBaLBZWVlWhoaCjIz+q6kPgvxa0vl84CgeBSAesycSvd5+vV1VUIhUK0tram9XkfM4QAv/EbZzep9EwUu+0hz/PA3h5QUgL86q8Co6MZGSolTdDVMoVyRyyHFvz67K9DKBDCoDTc6TkEjAC6Yh12vDv4w9M/xE9Ffgqm4usD94EfdAqzWq1wu91QKpUwmUxoaWmhi6M0U1xcDJZlwfM8fW8pKRiGQXd3NxYXF7G1tZUqu6KkD5vNhq2tLYyMjDy6Mu9QKAS73Q6Hw4FYLIbS0lLodLqCCsK/CzzPIxQKnRO+otEoGIaBRCJJCV7JctFYLIapqSk0NjZemzknkUjQ0dEBjuOwt7eHb775Bl6vN1U2+SXJBXA0Gr00ID5TuZOBQAChUCinxDDgLEtMJpPdKEssCcMwePLkCXZ3dzE5OYmhoaFHfWx/SV1dHXZ3d9HZ2ZnVcbhcLqysrKC3t/dCiXAu8WXnw+vKEBOJxI1FLZVKdUHkypW54KdPn8CyLM1xSzMMA/ydvwMIhcC/+lfAzg5gMt2882QwCBwdAQYD8I/+ERXD8gHqEKNQ7kAgFsDf+s7fwkfXR9Sr6+888Q0Gg2ATLJQlSmyfbuOH638Y/+df/j8hFFxui3a73bBarfB6vVCr1TCZTCgvLy/I3bpcYmVlBQaD4dE6VChXQwjB3NwclEolQqFQxrODHgNJ50g8Hkdvb2/OLD4ySTLE/csgfK1W+yg78rEsmypv9Pl8CAQCqTJHhUJxzu0lFouvvQYGg0HMzs5idHT0xu+lw+HAp0+fwPM85HI5zGbzhRzJvb09EEJQ90XQzMzMDHp6ejLSwGBiYgJdXV05mWl5F5dYEqfTidXVVQwNDdFmGd+HEIJvvvkma6WxhBCsr6/j9PQU/f39D1Ii+aWodd0tkUhceLxQKLyy3PBLUSvf5837+/twOBzo7+/P+78ll5maOnN4ra+fhexrNIBcftExxnFAIAB4PGdC2vPnwN/7e0BNTXbGTbkddCuGQrkD317/Nj44P6C6tPpuFyJyVgoEBmehpAxgUBrwZv8NJg4m8Lz2rNyC53nY7XbYbDaEQiFoNBrU19fTnJgHprKyEna7nQpilAswDIP+/n68ffsWkUgk28PJe+LxOGZnZ2EwGAq6QQHLsnC5XLDb7fD5fCgqKoJer0dbW1tOlEg9BIQQRKPRlPDl9/sRDAYBnC1sVSoVSkpKUF1dDaVSeWf3kEKhQF9fH6ampjA2NnajWAKPxwOz2QytVovT01Osrq6C53k0NzejoqICwNl1YX5+/oIgJpPJEAqF0i6IORwOyGSynBTDgLu5xJJotVr09/djeno6551IDwXDMKioqIDL5YL2PkFGdyAWi2Fubg5arRbDw8O3mm8SQsCy7I1ELZZlLzz+ss6HxcXFkMvl5wStQhC17srR0RFsNhtGRkYe7XvwUIyMAL/928Cf/AnwH/8jsLUFHB+fCWKEnN2Se3YKBfDy5Vmnypcvz4QxSn5AHWIUyi1heRZ/7d//Neyc7qCm9PbSPyEEPq8PoiLRhYXP1skWfqT+R/B32/8uDg8PEYvFoNPpYDKZHs0iKRfheT6VJ0OhXEYkEsG3v/1tjI+Pw2g0Zns4eYnX68XCwgK6u7sv7Q6Yz/A8j5OTE9jtdng8HjAMA61WC51Oh9LS0oJe1PA8j2AweE74uqzMUaVSQaFQZOy9ODk5wcrKCsbGxr4qrr19+xbDw8Pn7hcOh7GxsQGfz4f6+nqYTCa8efMG4+Pj51yMOzs7KCoqgsn09fiDm5J0C91U0MsW93GJAWdCzMzMDBobG1FVVZXm0eUfwWAQa2trGBwcfLDXdLvdWF5eTuWXfU3U4jju3OMZhrlS1LosJL6Qz32ZwOVy4ePHjxgdHaXdlh8YjgM+fjwTxXZ2gFDoTAwrLQUaGwGzGaitzfYoKXeBOsQolFvy7vgddk53oJXffseOEILT01NIJVJIZT/IxOE5HtFoFKKECH/+8c/xN4x/A93d3Y8uNydXEQgEEAgESCQStLMS5VKEQiFqamqwu7sLoVCIStpO6FYcHBxgd3f3VmVtuQwhBH6/H3a7HU6nExzHpYLw29raCrIMNJFInBO9/H4/eJ4/V+aYdDln4zMuKytDe3s7JicnMTY2duViMlm29aVoJpPJ0NPTg0Qige3tbXzve98DIQTHx8fnxBu5XI7T09O0jn1nZwcmkymnxTDgfi4xABCLxRgfH8fc3BwCgQDMZvOjFkwUCgWi0ShYlr21Q5IQcuPSQ47jQAiBx+NBOByG0WjE7u7uBQErKWBTUSs7eL1erK6uXnv+omQOoRDo6Di7UQoLKohRKLdkw7OBBJeARHS7CT3P8zg9PYVCroBYIgbHcYhGoojGfrBTbig3wOa3IaFKUDEsx9BqtXA6nXTXmnIpAoEAhBCMjIxgcnISAoEg54KvcxFCCJaXl8Fx3AWnTb4RDodTQfjRaBQlJSXQ6XSor68vGCE9WeaYDLT3+/0IhUIAzkqdkk6v+5Y5ZoqKigo0NzdjenoaIyMjlx5vgUDg2rLEoqIitLS0oLm5GR8/fsTr16/R1tYGs9kMqVQKuVwOm82WtjEnEgns7e3h5cuXaXvOTNLW1nYvl5hAIMDAwAA+fPiAhYWFR5MjeBk8z0Or1WJjYwM6ne7asHie5889lmGYSzseymQylJaWnvsdx3GYm5tDe3v7oxchc5VgMIiFhQWMjY0VzPWEQskVcmumQqHkAZueTYDBrSYMHMfh9PQUcpkc8UQcwWAQAqEAUokUZeoyMIIfPBcPHtsn23hW8ywTw6fckcrKSmxublJBjHIpAoEAPM9DKBRiZGQEExMTEAqFNHfuGmKxGGZnZ2EymS7kMOUD8Xg8FYQfDAYhk8mg1+vR09OT9y63ZJnj58JXLBYDwzCQSqUp4ctgMEAul+fVArqyshIsy2J2dhZDQ0MXxu52u1M5YdchEAjQ1tYGh8MBg8GAhYUFiEQiNDc3IxwOp228Hz58QGtra96IQvd1iQFn86v29nYcHBxgYmICw8PDeSsCXBcS/6W4dVnnQ6FQCKvVCkLIuTwttVp9TtS66/FxcnKCd+/eoauri16vcpRIJILZ2VkMDw9npFkHhfLYoYIYhXJLXGEXhMzNrcqJRAJ2ux3FRcWIxWKQSqVfzUnxxXzpGColjSiVyrNGCBTKJTAMk1rMiEQijI6OpjrClZWVZXl0ucfp6SkWFxfR09OTN+8Px3GpIHyv14uioiLodDq0tLRkNPsqk8TjcQQCgZTwlezmKBAIoFQqoVKpoNVq0djYWFALMZPJBJZlMT8/j76+vnOfncvlQmdn542eh2EYKJVKSKVSjI2Nwe/3Y2NjAzs7O2hoaEBlZeW9jotQKAS/34+urq47P0c2uK9LLEl1dTVkMhnevn2LwcFByOXyNI3w5nze+fAqd9bnAtdlotZl+Vlflh4WFRVdKWpNT0+jvr4+rR04CSHY2tqC3W7H2NhYQX2/C4l4PI7p6Wn09/fTDqwUSoagghiFcgduM8EVCoSQy+VnDhKORzAUBEJnzyEUClM3kVCUygSgvS5yj6QzIhwO00kJ5QKfC2LAWVlVUhTr7e096yZLAQDs7e1hf38/5xdhhJBUEL7b7U51faupqUFXV1feCGCEEEQikZTTy+fzpRxMIpEIJSUlUKlUqK2thVKpfDTZNHV1dUgkElhaWkJ3d3fq84xEIrc6x1dVVeHw8BAtLS1QqVTo7++H3+/HyckJ1tfXUVtbi5qamju9r8vLy3nZbTUdLrEkGo0Gg4ODmJ2dxZMnT+70fJ+LWjfJ1PrIASetAAB6hUlEQVQSoVB4qahVUlJyQdTKxHmhrq4Ou7u7aG9vT8vzJRIJzM3NQa1WY2xsLG/OZY8NlmUxNTWFrq4uqFSqbA+HQilYqCBGodwStUQNlrvYKvoqBELBpQ4Iwp9N0DiOA8uxiCQi4DgO4VAYR3tHeB1/DbFYDJlMBrlcfu7fXMtleSzo9XrY7XbU19dneyiUHOTLRUVxcXFKFOvv73/0E1qe57G0tASGYXIyL4wQgkAgkArCZ1kWZWVl0Ov1aGlpyXmhiOd5BAKBc6H28XgcAM6VOVZVVUEmk9FFMIDm5mZ8+PABa2tr6OjoQDwev3Vpnk6nw9bWFlpaWlI/k8lkaG5uRmtrK/b29vDq1Svo9fpbOe1cLldKdMlH0uUSA84aFYyOjmJ6ehoGgwEVFRXXilose3GOdlXnQ7lcfk7QypSodR90Oh0+fPiAtra2e48t6c598uTJjUqDKdmB53lMT0+jtbW14LouUyi5Bl1VUyi3pLm8OS3PwwgYiAQiiIpEEONsgszyLOREjh8d+lE8q32GWCyGcDiMcDic6lgWCoVSba4ZhoFMJrsgmkkkkpxbbBYCer0e7969o4IY5caIxWKMjIxgamoKAwMD14Z1FzLRaBSzs7Mpt0yuEA6HUzlg0WgUKpUKer0eg4ODOZtZFI/Hz4legUAg1c2xkMscM0VbWxuWl5exsbEBpVJ5awdSssve512I5XI5QqEQ1Go1GhoaUF9fj+PjY8zMzEAul8NsNl97LiCEYHV1FSMjI/f627LJZS4xQshXyw6Tt+Q853OKiorw/v17yGQy1NbWQiwWQywWQ6lUnhO1RCJRzola94FhGJSVlcHj8dzZcUcIwfb2No6Ojgqmm2+hQgiBxWJBbW0tbc5DoTwAVBCjUG5JU3kTRAIRomz01p0mv0YgFoCiWIGm8qZU50mJRHJlxg7P84hEIgiFQgiHw3A6nQiHw4hEIufyjL50mMlkMhQXFxfUhPEhkEgkiEajIITQ945yY6RSKYaHhzE9PY2hoaGs5OBkk2Ro89OnT7NeOhqPx+F0OmG32xEIBCCVSqHX69Hd3Z1TnX2TZY6fh9onyxyLiopSbq/HVuaYKZ48eYKFhQVYrVb09PTc+vGVlZWw2+0wmUwAzgtiwJmgYTAYYDAYcHp6irW1NbAsC7PZDI1Gc+F6sre3h8rKypwUNAkhNyo7jMfjiMViWFtbS4ngDMOkuh0m/y0uLoZEIrmQqZUUGi97/Y2NDfh8PvT39z+azb/6+npsbGzcSRBL5uUplUqMj4/T+UsOQwjB4uIiKioqUucTCoWSWaggRqHckqeVT1FdUo3DwCFMJem9WJ1ETvCi9gWqlDfrZCgQCCCXy69dYCcSCYTD4ZRo5na7EQ6HU6U0wJnQc5loRhdZFyktLYXP58v6wp6SX8hkMgwODmJmZgYjIyM5Jb5kkp2dHdhstqzlhXEcB7fbDbvdjtPTU4hEIuh0OpjN5pwIwuc47kI3x2SGkUwmSwlfRqORljlmEIZh8PTpU/zbf/tvEQgEbl2iZDAYsLKyck4Q8/kub46jVqsxNDSEcDiMT58+YW1tDfX19TAajRAIBGBZFjs7O3j58uW9/66r4Hn+qx0Pkzee5889lmGYC7lZxcXFkMlkKC0tvSBqLSwsoKamJm0dDBmGQUtLC2w2G96+fYuhoaGcFA7TjUqlSlUI3GZu5vV6sbCwgI6ODuo2ygPW1tYglUrR0NCQ7aFQKI8GKohRKLdELBLjJ1t/Er829WtgeRYiQXq+RuFEGAJGgJ9s+cm0LnqKiopQUlJyZQ4JIQTRaDQlmp2enuLw8BChUCg1ERYIBFeWZj62BZper8fx8TEVxCi3RqFQoL+/H1NTUwVfssLzPN69eweRSPSgjoRkEL7D4YDL5QLDMNBoNDCZTHjy5EnWzlfJMsfPuzkSQs51c9Tr9WhubkZxcXFWxvjYIYSkAvKLi4thMBhu/FiZTJZyZjMMA7lcjqOjo68+pru7G4lEAjs7O/jmm29gNBoRiUTQ0tLyVefTdSHxX4pbl3U+/Nyh9XmellqtPvez+zqw0pkl9jlJkXhiYuLRlKMnj8/q6uob3T+5IVHo15tCYXNzE4lE4k4uVQqFcneoIEah3IGfav0pfHv929j17qJeff88KZ7wsPltGDON4UXti/sP8BYkuydKpdIrd8U5jktlmYVCIdjt9lRpZpLkDvHnDjO5XJ6zOTx3RavVYnNzE62trdkeCiUPUalUePr0aUoUK0RnQzQaxczMDBoaGjJe8pEMwnc4HHA4HGBZFmq1Gnq9Hmaz+UFdroSQVN5jUvhKniOLi4tTbq/6+nooFArqwM0xfD4f1Go1Ojo6MDk5CZFIBK1We+PHl5eXpzKeZDIZQqHQtfcnhIDnebAsC71ej9LSUuzs7GB5eRk1NTU4Pj4GwzApgesyUeuykPgvSw+LioqyWlaYzo6TX1JWVobh4WHMzMygo6PjVp9XPlJdXQ2LxfJVQYxlWSwsLEAmk9ESyTxhf38fp6en6O/vz/ZQKJRHBxXEKJQ7UCYtwy8M/wJ+4c9/Ac6QE1r53SdhhBBYfVZUyCrw98f+PoqEuScgCYVCKJXKK3dgk0G5ybLMUCiUyjNLlv8kM9EuK83MpwwQoVCYauFOF7SUu1BaWoqenp6UKFZIjiC3243l5WX09fVlrDteJBJJBeFHIhEolUro9XoMDAw8yHvJcdy5bo4+ny/V1S5Z5lhaWorq6mpIpVK6GM0TXC4XNBoNhEIhRkZGMDExAaFQeG35ZPJaEI/HoVQqsb6+jtra2lRW3crKyjnX1pcIhcJz4pXb7U51YLVarRCLxWhpaYFWq83r4yhTLjHg7Dv37NkzzMzMIBQKoa6uLu2vkSuIxWIIBAJEIpEry+79fj/m5+fR1tYGvV7/wCOk3IXj42PYbDYMDw/n9fecQslXGPLllhOFQrkRhBD8xvxv4DfmfgOyYtmdRDFCCKx+K4oFxfgHL/4BvtX8rQyMNDdIhkQnRbOkcBaJRFKlmUKh8ILDTCaTQSwW59Qk4cOHDygvL6d5HJRzvH79Gs+fP7/x/d1uN1ZXVzE2NgaRKP/3p5IdzAYHB9MqTCUSiVQQvt/vh1QqhU6ng06ng0wmS9vrfEksFrvQzfHLMsfkrZBEzccGIQQsy2JycjLl/I3H4wiHw1heXobJZIJIJEI8Hk8Jn58jEolSTqzNzU309vaiuLgYKysrGBkZSf2uqKjo2uuYx+PB9vY2BgYGUj8LBAJYX19HOBxGU1MTKisrc+paeBvSnSX2JYSQVJl2Z2dn3r5PX+Pw8BB+v/9Sl/re3h729/cxMDDwaHIq8x232421tTWMjY3RTVYKJUtQQYxCuQeEEPzm/G/itxZ/CzE2BlOJ6cYOr0giApvfhjJpGf7Hsf8RP2b+sQyPNvdhWfacWJb8NxaLpe4jFosvZJnJZLIHFRROTk5wcHCA7u7uB3tNSu5zW0EMAJxOJz5+/JjXk2GO47C4uAiJRIKOjo57L0STQfgOhwMnJyep0jW9Xg+lUpnWhS4hBKFQ6JzwdVmZo0qlglKpzCs362Mi6VL+WvfDeDwOjuMuPL6oqAh7e3vo6Og459jieR7v379HX18fysrKIBKJrj3+ZmZm8OTJE8hkMkxNTaGvr+9GYikhBK9fv8bQ0NClWU+xWAybm5twOp2oqalBbW1t3p0vIpFIxlxin7O5uQm3242BgYG8e49uAs/zePXqFV6+fJk6FjmOw8LCAsRiMTo7O+l5Kk/wer1YXFzE+Ph4wcWLUCj5RP5vSVMoWYRhGPztvr+NDm0HfnXqV7Hp2USRsAhauRYS0cXAeUIIQokQnCEnCCEYqBrAL43+Ejq0HVn6C3ILkUiUWnxeBiEEsVgsJZr5/X7Y7fZU5yXg7DO5qgFAuiaJarUaKysraXkuSuFwF6FGq9WC53lMTU1hZGTk2gVcNArs7gJWKxCLAUVFgMEANDQA1zSazSiRSASzs7NobGyE0Wi803MQQnB6epoKwieEQKPRoKqqCh0dHWn53nIcd0708vv9KbePXC6nZY45ACHkWiHrc8HrS1GLYZhUSPznYfESieRCppZQKLzw+YbDYQiFQjx58uTCuDQaDaanpzE8PPzVRavBYMDR0REaGxshl8sRDodvJIgdHBxAq9VeGXwuFovR0dEBjuOwt7eHV69eQafToampKW9yCDOZJfY5TU1NUCgUqQ6UhRYmLxAIUFpaitPTU5SVlaVKJFtaWm7VCIKSXYLBIBYWFjA6OkrFMAoly1CHGIWSJnxRH769/m38wfofYPd0F3EuDgAQMGeLOZ6clQVKRVK0VbThp1p/Ct9q/hbEovyYzOYLPM+fK81M/pvsAAacCW+XZZkVFxffeCE8OTmJvr6+vFmMUDLPmzdv8OzZszs99vDwELu7uxgZGTknALEsMDMDfOc7wPQ0EAoBn/WygFh8JoY9eQL8xE8AL14AD7X+c7lcWFlZQX9//5Ui9mUQQhAMBlNB+IlEAqWlpdDr9aioqLiXqyMWi6UC7f1+P4LBIHieh1AoPOf2UqlUdBGSAXie/2rHw+QtWSqfhGGYC2Hwl4XGJ0WtdHJwcIBEIoGGhoZLfx8MBjE7O/vVbn2JRAIzMzMYHx/H1tYWpFIpqqqqrn1tjuPw6tUrvHjx4sZ/FyEEx8fHqddoaWnJiy6LD+USA87cNwsLCxnNM8wWXq8X29vbqKiowM7ODgYGBjJaPk5JL9FoFJOTkxgaGoI8W7tZFAolBRXEKJQ0k+ASWHYsY+tkC1snW/BGvRAwApRJy9BU1gSzxoz2inbqQMgiiUTiQllmOBxGPB5P3eeqBgDJBcv29jaKiopu3P6cUvjcRxADAKvVCpvNhqGhITAMg9VV4J/+U+DduzNhTKU6E7+kUkAgAAg5c42FQoDPd/Yczc3AL/4iMDaWpj/qEggh2NragtPpxMDAwI2EpWg0mgrCD4fDUCgU0Ov10Gq1txaVk2WOnwtfyTLHpCMoeVMoFLR86JYkQ+JvImxd1vnwOiHr81sufS6Li4toaGi4Vjjx+XxYWFjA2NjYta6vN2/eYHR0FE6nE36/H2az+drXXltbg0qlunNH1tPTU2xsbIBlWZjNZmg0mpyeX2Q6S+xzotEopqen0draWlAB8xzH4fd+7/fQ1taGrq6unPouUa4nHo9jYmICT58+LTihlkLJV6ggRqFQKF9ACEE0Gj0nliX/O+lq4DgOJycnaG9vv1CamcuLEUrmuK8gBpyFItvtDnz8OIDf/E0GPh9QVQXcZPM/FgMOD89cY3/jbwA///NAurPeOY7D/Pw8FAoF2trarjzWk0H4DocDPp8PEokEOp0Oer3+xk4GlmVT3RyT4hfLsmAYBgqF4pzwRb9357lK1LpM5LpM1LqJoFVUVFQwC/FXr17h+fPnXz2GTk5OsLKycm0jjPX19ZQYu7W1hd7e3iufL1lyfJPX/hrhcBifPn3C6ekpGhoaYDQac/LzeUiXGHD2XZidnYVWq0VjY+ODvGYmCQaDsFgskEgkqK6uvnOpOuXh4TgOb9++RWdn57XdaykUysNCBTEKhUK5AxzH4S/+4i/Q3d2dEsySpZlJiouLLzjM5HI5LdUqUNIhiBEC/IN/4MLv/q4carUUej2D266TPR7g9PSshPKXfzl9olg4HMbs7CzMZvOFrBqe5+F2u2G323FycgKhUJgKwlepVFcu9pO5gJ9neyXLHEUi0YVujo/lu0MIuZWo9SVCofBaIevz/3/sQiLHcZiamrqxQON0OrG+vo7R0dFLSxyD9mW41v4QNeoITvcmUK4SAwwDSLRA6RNA1QroXgDFpZidnUVTUxPKysrS9vckEgns7OzAZrPBaDSivr4+5743D+kSA86+TysrK+B5Ht3d3Xl7zFutVmxtbaUaBszPz2Msk3ZgStpIZoU2NjYWlFuRQikEaKg+hUKh3AGhUAiNRgO5XH7p5CbZ9ezzskyn04lwOJxawDIMc2VpZi7u7FMyz3/8j8C3v10BlSoCmcwPhlEBuN3irbz8LHD/O98BdDrg537u/uNyOp1YXV1Ff38/lEolCCHwer2w2+3ngvANBsOlQfjJ3LDLujl+Xuao1WoLpsyREAKWZW8kaiUD/j9HJBJdKmjJ5fJz4lZRUVHeLvBzAY/HcytBSqvVguM4TE9Pn8/8O1kAdv4fKNzTYHwOMGE5ijkOYEvPfh91AyfvAEYASCoQVv8liNiBtIphwFnHTLPZjKamJthsNkxMTECtVqO5uTlncqba2toe1CXGMAy6urqws7ODqakpDA4OPmhn6vvC8zyWlpYAAM+fPz93fozFYjTLNMchhMBisaCmpoaKYRRKDkIdYhQKhXJHbDYbIpEImpqa7vR4Qsi5BgBJ4SwSiaRKM4VC4aUuM7FYTBfBOcabN28wPj5+589lfx/4m38T8HoBkwkIBgPgeXKrwPrPcbkAjgP+5b8E+vvv9BQghODTp0/weDxoa2uD2+0+F4Sv0+lQUVGRWlyyLHuhm2OyI2A+ljkmhe2biFpfdj4EcM6NdV22lkgkyvn3olD58OEDysvLodPpbvU4q9WKw8NDDD7tALP9/wX2fhdIBACxBr4wA5lCDr/fj/KyL0qjeBYk5kbEb4e43Axh2y8ClT+CW1tBbwghBG63GxsbGxCJRDCbzVCr1Rl5rdvw0C6xJA6HAx8+fMDQ0BCkUumDvvZdCIVCsFgsaGxsvJAzZ7VaEQ6Hv5pTR8kehBC8e/cOKpWqIEp2KZRChApiFAqFckfi8TgsFktGSxZYlj0nliX/jcViqfuIxeJzolny33zaAS8EJiYmMDw8fOcOeH/v7wF/9EdAY+NZaD4ABAJ+AMydOsgRAmxvn3Wg/N3fBW57OIRCIbx58waJRAJKpRIKhQI6nQ5arRaEkHOiVygUAiHk0m6O2T4OCSHXlhteJ2oxDJMSsr4WFi8UCqmolYe8ffsWw8PDdzpOdz8tQ/7pl1FBPoApKgXEGoBhEIvHUsdXaakagi+Oi0g0gkQ8BpXQCzAioOlvA43/TcZEsSSBQAAbGxsIh8NobGxEZWVl1o7Zh84S+xy/34+5uTn09vbmhDh4FYeHh9jY2MDAwAAUCsWF3/M8j9evX+Ply5dZGB3lJqytrYFhGLS1tWV7KBQK5QroaolCoVDuSHFxMViWBc/zGSvxEolEKWHhMpIZTEnRzO/3w263IxQKpRb3DMOcc5d93gCgEErTcgWGYS4ElN+UvT3g9WtAo/mBGAYASqUylat12YLo+vEAlZXAx4+AxQKMjFx/f5ZlU0H4LpcLx8fHqK+vh16vRyKRgN/vx87ODnZ3d8+VOer1esjl8oweSzzP36jrYTweT7krf/A+MJeKWTKZ7FJRi/J4SGa13Um05aKo8/4m4rF3CAi1UCorUsXNxcXFCAaDEImKwHEsBKIfZHgREASDIWg05QBKgagT+PR/A0IJUP830/FnXYlSqURfXx9isRi2trawvr6Ompoa1NbWPvixL5VKIZPJ4PF4HjxgXKVSYWxsDNPT02hubr6QiZhteJ7H8vIyeJ7H8+fPr/xsBAIBlEolvF4vSktLH3aQlK+yubmJeDyO7u7ubA+FQqFcAxXEKBQK5R6Ul5fj5OTkwcs+kiRzyCQSyZVZNDzPnyvNTGaZRSKRlIAjEokulGUmBQPqerkZAoHgghhzU/78zwG/H2ho+PI3DFQqFXw+H0KhIOTy24licjlwdAT86Z9eFMR4nofH48HR0RGOjo6QSCRQXFyMaDSKk5MTVFdXQy6Xg2EYaDQa1NfX36tU97KQ+KtErcs6H14masnlcqjV6nM/oyIv5aYEg8E7uS8BANv/GnB+g+KSesSiHILBAJSKs+diwIBhGAgEDFiWQ9FnglgwGDr7XiXlM4kWiBwDn/4FoO4C1N33/Ku+jlgsRnt7O1paWrC3t4fXr19Dq9WiqanpQfOoHjpL7HPEYjHGx8cxNzeHQCCQM2WH4XAYFosF9fX1qK6u/ur96+rqsLu7i56engcYHeWmHBwc4OTkBAMDA3QORaHkOFQQo1AolHug1+tht9uzJojdBIFAALlcDrlcfuV9EonEubJMj8eDcDiMeDyeus9VDQCoq+aM+whiCwuAUHjeHfYDGJSUlMDr9SEcDkEmu/pzvAy5HLBYCEKhCI6ODmGz2eBwOBCLxSCVSlFaWorKykqUlpbC7XYjFovhW9/61qWumdt0PrxM1Lqs1FCpVF7ogkhFLcpD4HK57nbu9q4CO/8PIFIBIhkUirMyvGAoCMX3RWuJRIJ4/P/f3n0Ht9Wn96H/ngOAqCTYAYqiKBaRFFUpUhIlFu1m73o38XWNYyf3xjfFdux47MnG9l17xp4UXycuyTpxSWacONnJJhlfZz1e93LXu/u+aqyiJFISy0uKkihKAAESLOjAOb/7x3mBJSVSYkHH9zPDUSFw8CNFATjf8zzPLwpJ+ubPsqKqiETCqKp64zFNTsC/ADz+ZeDaF7VqsQzQ6XRoaWlBc3MzXr9+jZGREZjNZnR0dBw+KDyAbFaJAdrXf/XqVTx+/Bj37t1DV1dXVp97Xr16hZmZmeTmJftRWVmZrCbj82ZueP36NV68eIHr168zDCPKA5whRkR0BKqq4tatW7hx40a2l5JWQgiEw+EdoVni94kQSJblPVszi+FN4ejoKM6dO3fgQc2xGPDpTwPBoLYr5N4EfL51mExGmM277xantYDFEY/HEYtpOxyur6sIhyV87nMfoqXFBKfTibq6OpjN5mSFVigUwpMnT2A0GlFVVYVYLJbcDXU7nU635wytNyu4iuHfnPLb6Ogozp49e/DdFx/8LLD0e4DtVHLulwCwsbEOg6EEVosFiqrC51uDwWCAvcwOAFjfWIfFrFXevkUJA6FXwKUvAMc+e8Sv7PB8Ph9mZ2cRj8fR1taGmpqatP5fDgaDuHfvXlaqxLZ7/vw5Xrx4gd7eXhgMhvffIYVUVcXU1BSi0SguXbp04ItM09PTsNvtOdf6WYy8Xi8eP36M/v5+XiwkyhOsECMiOgJZliHLMmKxWMbfRGeSJEkwm80wm817XslXFGVHUOZyuZKtmQmJ2U1vtmcWwvfusBViPp8WhpneWxQioby8HOvrPgghIElyMriKx+MQQoWqio9/VSHEN6uywmEzLJaTsNn8CIfDWF5eTgZX8XgcCwsLOHXqFOrq6nYEXAy1qJAFg8GDh2HBV4D7a0BJ5Y4h+BIAu137/yl//HwJAPG4NssxFo9BVcXuYRjwcVWYAJZ+P627Tr5PRUUFent7EQwGMTc3h8ePH6OlpQXHjx9PSwVS4nUgW1ViCY2NjbBYLLh16xauXr36zorqVAqFQhgdHU3OcjuMkydP4v79+wzEsmxjYwOTk5MYGBhgGEaURxiIEREdkcPhwMrKCurr67O9lKzS6XQoLS3ds9VDCLGjNTMQCCTnmSWqkRIz0XZrzcz1dpDDBmKKou0IuZ/zX0mSUF5eAZ/Ph2g0Cr1el/x7ISQYDDoYjUYYjaaPWw8lRKMSFAW4cKEbly/vPN7r168xPT2NT3/60xk7ASTKBdFo9HBB/OoIENsArM1vfUoCUF5eDt+aD5IswWg0IhgMQkBrqbTby9997JJKwPdQG7Rvfme5aNpZLBZcvHgxGZh/4xvfQH19PVpaWlJ+AeP06dM5USVWU1ODK1euYHh4GBcvXkx7QOdyufDkyRP09PTsuXHOfpjNZsTjWkXwnoErpVUgEMD4+Dj6+voK4gIfUTFhIEZEdEROpxMfffRR0Qdi7yNJUrL6aK8dsYQQOzYAWF1dxYsXLxAKhZJhk06n27XK7CgD31PhsLtMmkza/LCPNwV9p0SoaDDok4+lBWAlH78Jf/vrVxTt+Ns7OYUQePLkCfx+/zt3MSMqVF6v93Dzw7Y+0vojpd0DegkSKiorsLa2BpPJhHhcQTgchsFggP59/8/0ViC4DPjnsx6IJej1erS3t+PUqVN4+fIlbt++jfLycrS3tx+8um4PuVIlBgA2mw0DAwMYHh5GU1MTGhoaUv4YQgg8evQIwWAQg4ODh9vl9A2NjY148eIFWltbU7BCOohwOIzh4WH09vbC9P5SbyLKMQzEiIiOqLS0FJubm9leRkGQJCl5crSXeDy+ozVzfX0dgUAAkUgkeRuj0fjWLDOLxZKSE4+9HLZCrLwcqKkBlpeBioo3PysQi8URiUQQjUYghIDBUAKj0QibrXRfAWAwqIVhjY3an2OxGEZHR5PVEGyLpGLk9XoPdxFjcwaQ3/08IkFCRUUl1tZWoShx+P1bbw/S341cAkAB/M+Amr6Dry2NZFnGiRMn0NDQAK/Xi4mJCeh0OnR0dKDi7SeuA8uVKjFAa+3v7+/H+Pg4tra2cPr06ZQ9T4bDYYyMjKChoQHnzp1LyTEB4Pjx47h16xYDsQyLRqO4e/cuenp6WGVNlKcYiBERHVEixDnUPBo6ML1ej7Kysj1bTIQQiEajySqzzc1NuFwuBAIBKB+XYW0P3t7cAOCwrZmHDcQkCTh/HvjoI+3PiqIgEgkjEolAUVQYDAYYjSWwWCoOtTa/H+jsBEpLga2treTw/9ra2gMfi6hQ+Hw+nD179uB3jAcA6f1vn2VJQmVlJQKBIIxGI+R9ByoSoITef7MskSQJNTU1qKmpwdbWFmZnZxEIBJIzCA8bHOVSlRigPZ9fvnwZMzMzGBsbQ09Pz5Hb9t1uNx49eoSenh7Y7fYUrVSTqJze3Nw8Uvsl7Z+iKBgaGsL58+dT/u9JRJnDQIyIKAWcTidcLheam9+eK0OZJUnSx22ERlRWVu56G1VVd7RmJmaZhUKhZCuiXq9/qy3TYrHsuYOiLMuHapmMRqNoa9tAJGLD69dBmEwyTCYj7HY7ZPlorYyqCkSjwKc+BSwvL2Nubg7Xrl1jcEtFbfvOuAcm6aD1TL6fLMloPHHikI+R+0pLS9HT04NIJIL5+XlMT0/j5MmTOHny5KHasHOpSgzQXktOnz6NpaUl3Lp1C9euXTvUjC4hBB4/fpxsUU9XpXJTUxMWFxdx4cKFtByfvklVVQwNDaG9vf1wrddElDMYiBERpYDD4cDExAQDsTwhyzKsVus7Wxy2bwCQmGcWDAYRjUaTt9m+AcDGxgasVivKy8vfeTKoKAo8Hg9cLhfW19dhMBhw6ZITzc0VWFurREVF6loY19YAu12guXkWy8ubGBwc5LwwKnobGxuHr+iwHAd891O7oAShajtsmGrSc/w0MRqNOHPmDDo6OvD8+XN8+OGHqK2txalTp2A0Gvd9nFyrEktoaGjYsQOlzWbb933D4TBGR0dRX1+PM2fOpLVFvbq6GlNTUx/vQsxW+HQRQmBsbAwnTpyA0+nM9nKI6IgYiBERpYDJZEI0GuUb0QJiMBhgt9v3PHEWQiAcDifnmQWDQTx79gxLS0s7KlDMZnOyIi0YDKKkpATHjh1DY2MjLly4kPx5+Xt/D/ilXwJCoZ0D8A8rFgNWV1XcuPECjY16tLZeOfpBiQqA1+tFTc0hQ6eyDmD5j1O7oAQlBOhNgC0/50DpdDo0NzejqakJLpcLIyMjMJvNaG9v33cbX65ViSVUVVXh2rVrGB4exrlz5/b18+PxeDA5OYnu7u49N5JJJUmSUFtbC7fbzaAmTYQQuH//PqqqqnDiMNWfRJRzGIgREaVIeXk51tfXUzJgmHKfJEkwm80wm82oqqpCMBiE3W6Hw+HA1tYWXC4XXC4X1tbWYDabYbPZUFFRgXA4jJWVFaysrADQBjhbLBZ0d1tw/vwx3LtnQkuLBIPh8PNqhAAWFxVUVa3hp3+6FK2tuVNtQZRtHo8H3d3dh7uzvROQDEA8COhT3HocXdeqw2xNqT1uhkmShLq6OtTV1cHn8+HJkyeIx+Noa2tDTU3NOy8a5WqVGKCtLbEDZTAYRGNip5I3CCEwPT2NjY0NDA4OfrwDcGY0NTVhamqKgViaPHnyBEajkZsXEBUQBmJERCmSmCPGQKz4BINBeDweLC4uwmg0oqysDE6nE9euXXvnyZAQYkdr5g/9kAfPn1dielqPuroQEiOOdDoZOp0eOp1ux8duJ5ZCAB99FIXBEMIv/VIpWlu5DTzRdtFo9ECtfDtU9gClp4CtOUB/MnWLEiqgBIDjPwDoDrm2HFRRUYHe3l6EQiHMzs7i8ePHaG5uRkNDw54z3HK1SgzQKof7+vpw//59bG1tvdUGGYlEMDo6CqfTid7e3oxXjFutVkSjUcRisYwGccVgfn4ekUgEXV1d2V4KEaWQJA4zAZiIiN6iKAru3LmDwcHBbC+F0iwajWJlZQUulwtbW1swm81QFAVOpxMtLS1HOvajR8BP/iTw7Blw7BhgswkoigpFiUNRFCiKgnhcgaoqSLyCS5IEnU6HeFyPly8Buz2OX/kVIz77WSNbeIm2CYVCePjwIXp7ew9/kGe/Azz6fwBzPaBLUeAcdgOSDPT9v4DtZGqOmYPi8TgWFhbw8uVL1NfXo6WlZdfg5t69ezh58mTOVYltNzc3h7W1NVy+fBk6nQ5erxcPHz5EV1fXnhu6ZMLi4iKEEJxpmkIvXrzA69evceXKFb6mEhUYBmJERCl08+ZN9PX1cXB5gVEUBV6vFy6XCz6fD3q9Hg6HA06nEzabDZIk4enTpzAYDGhoaDjy4z17BvzyLwM3bwKyDNTWAu/aGDIcVuFyqVhfD6OjI4gf+ZEN1NevIhKJJG9jNBp37JaZ+H26djwjykVLS0uIRqNHC66VMDDyj4DVUW3e11FPkJUwEFwC2v8J0PaPj3asPCGEwNLSEp4+fQq73Y729vYdu98Gg8GcrRLb7tWrV5idnUV1dTU2Nzdx+fLlQ+1EmUrxeBx37tzBjRs3srqOQuFyuTA/P4/r168fbmdaIsppDMSIiFJoenoaFRUVnN+R54QQWFtbg9vthtfrBaDt4OV0OlFRUbHrFeJnz55BkqQ958ocVDwO/MEfAL/zO8DcnDYkX6/XgjFZBlQVCIcBbdNLBWVlPvzdv2vCD/2Q7a2h/EIIRKPR5I6Z239VFAWAVmW2PShL/GoymXgSQAVjYmICLS0th99lMmH9ETD6w0BsE7A0Hj4UU2NAYBGougJc/c+pqzjLIx6PB3Nzc5BlGR0dHcmxA/lQJRaNRnHz5k2sra3hM5/5zNF/rlJkeHgYZ8+ePdCOmPS21dVVPHr0CP39/bzQSVSgGIgREaWQz+fD8+fPcfHixWwvhQ5ACIGtrS243W643W7E4/FksFldXb2vN8IvXryAoihoakrtQOx4HBgeBiYmtHbK+XktBNPpgMZG4MQJH8rLl/CDP9iG8vLDn0wndsJMhGWJwCwUCiHxVkGv179VYWaxWFBSUsI2EsoLH3zwAW7cuJGan9fXXwUmfw6I+QFrIyAd8IRZCWmVYfYzQM9vAJb6o68pj21tbWF2dhaBQACtra0oLy/HxMREzlaJra6u4sGDB7h48SIsFguGh4fR2dkJh8OR7aXB7XbD4/Hg7Nmz2V5K3trY2MC9e/fQ39+f9ao/Ikof9kkQEaVQeXk5Hj58mO1l0D6EQiG4XC643W6EQiGUlpbC6XTiypUrh3rzK8sy4vF4ytep1wP9/dpHgpZPCTx8+BCqquLixYtHruKSZRlWqxVWq3XP22zfACAYDGJ1dRXBYBBRrUwNAGAymXYNzXh1nbJNURTIspy68Lbu04BcAjz6BcA/DxgdgMH+/moxoWgzw5QgUH0duPCvAMux1Kwpj5WWlqKnpweRSATz8/OYmZlJzmusra3N9vKShBCYm5uDx+PBwMBA8vViYGAAIyMjCAQCWZ/fVVtbi8ePH0MIwYsVhxAIBDA+Po6+vj6GYUQFjhViREQpdvfuXVy6dAkmU/G1vuSyxImV2+3G5uYmzGYzHA4HHA7Hjtk1h7W8vIxQKJSR7dgjkQhGRkbQ0NCQ8oq0oxBCIBwO76gwS/w+0Zopy/KerZk8caN08ng8WFlZwZkzZ1J74NBrYOZXAdfXgHgA0JcBBhugs2iD8gGtNTIeBGIbgBoBjFVAyw8CJ/9PQMcT7t0oioK5uTmMjIygp6cHp06dOvzuoCkSjUYxNjaGyspKdHR0vPWcJYRIXhS7cOFCVp/TJicn4XQ6cypMzAfhcBh37txBb2/vOy8QEVFhYCBGRJRiCwsL0Ov1KZslRYeTGITvdruxtrYGvV6P2tpaOJ1OlJaWpvxE5fXr19ja2kJbW1tKj/smn8+HiYkJXLp0KTlrJ58oirJrYBYKhZK3KSkpeSs0S7RmEh3WkydPUFVVlZ6WNiGA9Slg+U8A11e12WLxIJB8mpEBvRUw1wEN3wXUfRYwc9bkfoyNjcFms8Hj8cBsNqO9vR1lZWUZX0fiuffChQuorq5+520XFhbgcrlw9erVrG1csrW1hSdPnuDq1atZefx8FIvFcOvWLXR3d+fMPDgiSi8GYkREKRYMBjE1NcU3oRkmhIDP50vOThFCJAfhV1ZWpv1Kvdvths/nQ0dHR9oe49mzZ3jx4gWuXr2a9UqJdBFCvNWamfg1FosB0DYA2N6auT004wYAtJdbt27h2rVr6Q8o1Bjgf6YNy48HtCqxkkqgtBUwOY++K2WR2b7j5Pr6OmZmZhCPx9HW1oaampq0P7cLITA/Pw+Xy4UrV67s+7nX5XJhenoavb29ML+500mGfPDBB+jv7+duwvugKApu376NM2fOvDfwJKLCwWdHIqIUs1gsCAaDnN2RZkII+P3+5CD8WCyG8vJyOJ1OtLW1ZXxmlSRJSNc1JlVV8eDBA0iShP7+/oIOfSRJQklJCUpKSlBeXr7rbYQQCIVCybBsdXUVS0tLCAaDUFUVAKDT6d4Ky6xWK4xGI/9fFiEhBBRFyUwwIBuAslPaBx2ZxWKB2WzG6uoqqqqq0Nvbi1AohLm5OTx+/BjNzc1oaGhIy/NiLBbD+Pg47HY7+vv7D/Tc4XQ6YbFYcPfuXXR3d+/5fJZODQ0NePnyJU6ePJnxx84nqqpiaGgIbW1tDMOIigwrxIiI0mB8fBxtbW1ZaesoZOFwGG63Gy6XC8FgEDabDU6nEw6HI+vtdIn2zFTPJwqHwxgZGcHJkyfZhnsA8Xj8rdbMQCCASCSSvI3RaNw1NGM1ReFJ7GDY09OT7aXQIWyvEtsuHo9jYWEBL1++RH19PVpaWmAwGFLymOvr67h37x7OnTt3pDlc4XAYw8PDaGtrw7Fjmd08IRqNYnh4GIODgxl93HwihMDo6Cjq6upw4sSJbC+HiDKM7/iIiNLA6XTC5XIxEDuiWCyWHIS/sbEBk8kEh8OBc+fOpWQQfiqlo0JsbW0N9+/fz1p1QT7T6/UoKyvb8/+gEALRaDQZlm1ubsLlciEQCCQ3AJAkaddZZmazuaCr9AqR1+tFTU1NtpdBh/RmlViCXq9He3s72tra8PLlS9y5cwd2ux3t7e2Hfo0QQuDp06dYXl5GX1/fkTfIMZlMGBgYwOjoKAKBAE6dylzlYElJCQwGA4LBYM69ZuYCIQQePHiAyspKhmFERYoVYkREaRCLxTAyMoL+/v5sLyWvqKoKr9cLl8uFtbU16HS65CD8srKynG518/l8WFpawvnz51NyvKdPn+Lly5cFPS8s16mqilAotGP4fyAQQCgUSoafer3+rQqzxAYAufzzWmxGR0dx5swZ7hqXx/aqEnuT1+vF7OwsZFlGe3s7Kisr9/0Y8Xgc4+PjsNlsOHPmTEr/Dwsh8OjRI8RiMXR1dWXs+eH169fw+Xzo7OzMyOPlkydPnkAIkfqdZ4kob7BCjIgoDQwGA+LxOFRVZSXJOwghsL6+DpfLtWMQ/rFjx3D27Nm8+t7JspycX3UUqqri/v370Ov1GBgYYKiSRbIsw2q1vjNEeXMDgNXVVQSDQUSj0eRttm8AsD00y/Scu2LGCpn8t1eV2Juqq6tRXV2dbJOdmppCa2srjh079s7n042NDYyPj+Ps2bNp2YlUkiScO3cOz549w507d9Db25uR9myn04np6WmcPn2aryfbzM/PIxwOo6urK9tLIaIsYiBGRJQmVVVVWF1dZZvOG/x+P1wu145B+A6HA6dOncrr2U2yLB+5ZTIUCmFkZAQtLS1oaGhI0coonQwGA+x2O+x2+66fF0IgHA4nK8zW19fx6tUrBIPBZGumLMtvhWVWqxUmk4knsCkQjUZhMBj4vSwAnZ2d+6oSA4DS0lL09PQgEolgfn4eMzMzaGxsRFNT01th9OLiIl68eIHr16+nfUfIkydPwmq14ubNm+jt7U17UCtJEiorK7G2tvbOILGYLC0twev14urVq3xeICpybJkkIkoTj8cDl8uFc+fOZXspWZUYhO92uxEIBGCz2eBwOOBwOAqqFdDv92N2dhbd3d2Hur/X68XDhw/R09OzZ7hChUlRlGRgtr3aLBwOJ0PWkpKSXeeZZXsziXzw+vVrbGxsoKOjI9tLoRQYHx9HU1PTgcMdRVHw/PlzPHv2DDU1NTh16hQMBgPu3bsHk8mEc+fOZTQc2drawujoKLq6ug7U1nkYGxsbmJubw+XLl9P6OPnA5XJhfn4e169fz6sqdCJKj/y9FE9ElOOqqqrw+PHjbC8j4+LxeHIQ/vr6OoxGIxwOR8HP7zlKy+TCwgJev36NgYEBBhxFSKfTobS0FKWlpbt+XgixozUzEAhgZWUFwWAQsVgMgFYFsr01c3toVuwnfR6PB/X19dleBqXIQarEttPpdGhubkZTUxNcLhdu3boFt9uN3t5etLa2pmm1eystLcXAwACGhobQ0tKC48ePp+2x7HY7/H4/FEUp6lbt1dVVzMzMYGBgoOifF4lIw0CMiChNZFmGXq9HLBZL2TbwuUhVVayursLlcmF1dRU6nQ41NTVoamqC3W4vmnaEwwRiiqJgYmICJpMJfX19RfO9ooORJAklJSUoKSnZc7dRIQRCoVAyNFtdXcXS0hKCwWDy51Kn070VllmtVhiNxoL+2fP5fDh79my2l0Epst9ZYnuRJCnZRvupT30KL168gMvlQltbG2pqajL6f6GkpAQDAwMYHx+H3+9He3t72h6/vr4ey8vLRbub4sbGBh4+fIj+/v6iDgWJaCe2TBIRpdHc3BwsFktar/xmmhACGxsbyUH4qqqiqqoKDocDVVVVRXvVNRKJ4P79++jt7d3X7UOhEIaHh3Hq1KmC+vmg3BWPx99qy0y0ZiYYjcZdQ7N8ne8nhMCHH36IT3ziE9leCqXQfnecfFPiIoTBYMD58+eTr1ehUAhzc3NYW1tDc3MzGhoaMvpaJoTA9PQ0AoEAuru70/LYkUgEo6OjB/6eFYJAIIDh4WH09fXBZDJlezlElEPy890NEVGecDqdmJuby/vAIxAIJAfhR6NR2O12OBwOtLa25u2JcqodpELM4/FgcnISly9fRllZWZpXRqTR6/UoKyvb82dOCIFoNJoMyzY3N+FyuRAIBJIbAEiStOssM7PZnJNh+Pr6+p5VdZS/DlMltrW1hbGxMXR0dODYsWM7Pmc2m3HhwgXE43E8ffoU3/jGN1BfX4+WlpaMVHhLkoTOzk68ePECt2/fRm9vb8rb541GI2RZRjgcLqpQKBwOY3h4GL29vUX1dRPR/rBCjIgojYQQ+OCDD/DJT34y20s5kEgkArfbnTwZtlqtcDqdqK2t5RvKPSiKgqGhIfT39+95GyEE5ufnsbKygitXrhR0Ky0VJlVVEQqFdlSYBQIBhEKh5AYAer3+rQqzxAYAmW7N/Oijj2A2m/P+ogS97SBVYi9evMDTp09x+fLlfc2yFELg5cuXWFhYgN1uR3t7e9p3g0xYXV3Fw4cPcfXq1ZTP3VxeXsbW1lbRbDARi8Vw69YtXLp0icE4Ee2Kl/WJiNIoUU0RDAYz9mb6MOLxeHJXzO2D8Ds7O2Gz2bK9vLwgSRLedY1JURSMj4/DZrPh+vXrBT2ziQqXLMuwWq3vPFHfvgFAMBjE2toaAoEAotFo8jbbNwDYHpqleraP1+tFV1dXSo9JuWE/VWKKouDBgweQZRmDg4P7rmKUJAkNDQ1oaGiA1+vF/fv3Icsy2tvb074jZFVVFa5evYqRkRGcP38e1dXVKTt2XV0dZmdniyIQUxQFd+/exfnz5xmGEdGeGIgREaWZ0+nE69ev0dLSku2lJKmqirW1teQgfFmWi3IQfiq9KxALBoMYGRlBe3v7W606RIXGYDDAbrfDbrfv+nkhBMLhcLLCbH19Ha9evUIwGEy2Zsqy/FZYZrVaYTKZDvT8FIlEWNVawN6146Tf78fY2NiR5zRWV1ejuroafr8fs7OzmJqaQmtrK44dO5a210qr1YqBgQEMDw/jxIkTaGxsTMlxZVlGeXk5fD4fKioqUnLMXKSqKoaGhtDW1pbSQJGICg8DMSKiNHM6nbh3715WAzEhRHIe0MrKClRVRWVlJZxOJzo7O3Ny9k++2evEaGVlBY8ePcLly5dRWlqa4VUR5R5JkmA2m2E2m99Z2ZMIzILBIFwuV3IDgETwXFJSsus8s8TspVAoxDCswO1VJfby5Ut89NFHuHLlSsraDm02G7q7uxGNRjE/P4+ZmRk0NjaiqakpLbsWGgwG9PX1YWJiAn6/H52dnSkJ4JqamrC4uFiwgZgQAmNjY2hoaEBdXV22l0NEOY4zxIiIMuCDDz7AjRs3Mlp5lTiJdLvdCIfDsNvtcDqdqKmp4eyqNLl58yYGBwcBaG/K5+bmsLq6iitXrnDzAaIUEkK81ZqZ+DUWiwEANjc3IcsyTp48+VZoxosAhWP7LDFVVfHgwQMIIdDV1ZXWf2dVVfH8+XMsLi6ipqYGp06dSksAm3gt8fl8uHz5ckrCt69//ev4xCc+UZD/Dx48eACr1YpTp05leylElAcYiBERZcCDBw/Q2NiY1iuykUgEKysrcLlc8Pv9sFgscDqdcDgcrJLIkEQgFo/HMT4+Drvdjo6ODragEmXBxMQEjh07Br1ev2MTgGAwmNwRVqfTvRWWWa1WGI1G/r/NI+Pj43A6nZifn0dLSwsaGhoy9thCCLjdbszNzcFkMqGjoyMtuwcvLy9jfn4evb29MBqNRzrWzMwMbDZbwW028eTJE6iqirNnz2Z7KUSUJ3i5mogoAxJzxFIZiCmKsmMQvsFggMPhQEdHB2w2G0/msiQQCGBkZASdnZ1wOp3ZXg5R0drc3ERXVxckSdpzjlA8Ht+xW+b6+nqyNTPBaDTuGpqx6jN3VFRU4Gtf+xq++7u/O+Ot6ZIkwel0wul0Yn19HdPT04hGo2hra0NtbW3KXovr6+thsVhw+/ZtXLly5Uhf58mTJ3Hv3r2CCsQWFhYQDoe5iQYRHQhfyYmIMqCmpgZzc3NHOkZiEL7b7YbX64UkSaipqUFjYyMuXLjAACwH+P1+jIyM4MqVK9ydkyiLFEWBLMvvfV7U6/UoKyvbs6JHCIFoNJqsMEvMYgwGg4jH4wC+uZvwm6GZ2WwuyJa0XKKqKiYnJxGPx3H69OkdO5lmQ3l5Oa5evYpQKIS5uTk8fvwYzc3NOHHiREp+FioqKnD9+nUMDQ3h7NmzqK2tPdRxTCYTVFVFJBI5crVZLlhaWoLH48HVq1f5XoiIDoQtk0REGXLz5k309fXte/5HYhC+2+3GysoK4vF4chB+VVVVWob40uEIITAzM4OJiQl83/d9HytHiLLM4/HA7XZnpHVKVVWEQqEdbZmBQAChUCi5AYBer3+rwiyxAQBP4A8nGAxidHQUTU1NaGxs3DFLLFfE43E8ffoUS0tLOHbsGFpaWpKbPhz1uMPDw6ivr0dTU9OhjrG0tIRQKIS2trYjryebEu2qfX19DKCJ6MAYiBERZcj09DQqKire2UYXDAbhdrvhcrkQDodRVlYGp9OJ2tpaDsLPUfF4HGNjY6isrITL5cKNGzeyvSSioref59tMenMDgMTvt1c0mUymXUMzXvx42+vXrzE9PY2enp4d1X3j4+Noamrac/fSbBFC4OXLl1hYWEBZWRna29uPvPulEAIPHjyALMs4f/78gYNVRVFw8+ZNfPKTnzzSOrJpdXUVU1NT6O/v54UoIjoUBmJERBni8/nw7NmzHfMtotFochD+1tYWzGZzchC+2WzO4mppP7a2tjA2NoYzZ87A4XDs2GWSiLLn9u3b6O3tzZuTZCEEwuHwjrAs8XtFUQAAsiy/FZZZrVaYTKaiqTITQmBqagrhcBjd3d1vhYW5WCX2Jq/Xi9nZWciyjPb2dlRWVh7pePPz81hZWcHVq1cPHJ6OjY2hra0Ndrv9SGvIhs3NTYyPj6O/vz8lVXdEVJzy410CEVEBKC8vh8/nS1aA+Xw+6PV6OBwOtLe3cxB+nnn16hVmZ2fR29sLi8WS7eUQ0ceEEIjH43kThgHaHDKz2Qyz2bxndZOiKDt2ykzMMguHw8nWzJKSkl3nmRVCYBAKhTA6OooTJ07g/Pnzu94mMbttdXU156rEEqqrq1FdXQ2/34/Z2VlMTU2htbUVx44dO9R7gNbWVthsNty6dQu9vb0H2lW6ubkZi4uLuHjx4oEfN5sCgQDGxsZw/fr1gvjZJqLsyZ93CkREeUgIkRyE7/F48Pr1a1RVVaGhoeFQLQ6UfUIIPHnyBH6/H4ODgzuuyLPomij7/H5/QW5qodPpUFpauufugkKIHa2ZgUAAKysrCAaDiMViyduZzeYdFWaJ0CyX5y+53W48evQIPT09761m6uzsxMTEBPr7+zO0usOx2Wzo7u5GNBrF/Pw8ZmZm0NjYiKampgNXejmdTpjNZty5c2df36OEyspKPHz4EEKIvHk/Eg6HMTw8jKtXr7KSnoiOjC2TREQpJITA1tYW3G433G434vF4co5NdXU1nj9/Dp1Oh8bGxmwvlQ4hFothdHQUtbW1aG1tfesE4sMPP+QMMaIsW1xchCRJOHnyZLaXknOEEAiFQm+1ZQaDQaiqCkAL3t4My6xWK4xGY8ZDEyEEHj9+jEAggO7u7n1X/Y2Pj6O5ufnI7YiZpKoqnj9/jsXFRVRXV6Otre1A1V7AN8Oijo6Ofc/Pe/LkCSoqKlBXV3eYZWdULBbD7du30dXVhfLy8mwvh4gKAAMxIqIjCoVCcLlccLvdCIVCKCsrg8PhQG1t7Vul/MFgEJOTk+jt7c3SaumwNjc3MTY2hvPnz6OmpmbX23CGGFH2jY2NobOz88hDy4tVPB7fdZZZOBxO3sZoNO4amqWyTTUcDmN0dBTHjx9Hc3Pzge4bDAbzokpsN0KI5M6JJpMJHR0dOzYOeB9FUTA6Ooqamhq0tra+9/bBYBAPHjzA9evXj7LstFMUBbdv30ZnZ+eer8FERAfFlkkiogNKDMJ3u93Y3NyE2WyGw+HAhQsX3lu+b7FYEAqF8qo9gYDl5WXMzc3h2rVrnBdGlOMCgQD/nx6BXq9HWVnZniGMEALRaDQZlm1ubibnmcXjcQDaTLTdZpmZzeZ9tWaurKxgamoK3d3dh6oEslgsMBqNWFtby6sqMUD73jmdTjidTqyvr2N6ehrRaBRtbW2ora1973sHnU6H3t5eTE1N4f79+7h48eI772OxWBCPxxGNRnN2HpcQAsPDwzh16hTDMCJKKVaIERG9h6Io8Hq9Owbh19bWwul0orS09MDB1r1793Dq1KkDXfGl7BBC4NGjRwiFQrvuaPYmVogRZVcsFsPIyEheVgYVElVVEQqFdlSYBQKB5AUhQAve3qwwM5vNWFhYwObmJnp6emAwGA69hnyuEntTKBTC3NwcVldX0dzcjBMnTuwrWFxcXMTy8vJ7d1x99uw5Hj6U4PGcwOwsMDsLhMNASQlw8iTQ2QmcPw/09gKZ3qtCCIGxsTE4HA6OmyCilGMgRkT0BiEEfD4fXC4XvF4vhBCorq6G0+lEZWXlkSu7lpeX4ff70d7enqIVUzpEo1GMjo7C6XTuq+0EYCBGlG0ulwvr6+vo6OjI9lLoPbZvABAMBrGxsYGZmRmYzebka63JZHorNLNYLPseOj82NoaWlpa8qxLbSzwex+LiIl68eIFjx46hpaXlvVVdKysrePTo0a47IgsB/OVfAv/jf6gYGQlAr9c2bDCbAZ0OUFUtGFNVLQhrbQW+7/uA7/5uLSzLhAcPHsBqteLUqVOZeUAiKioMxIio6Akh4Pf74XK5sLKyglgshoqKCjgcDtTU1Bx4t6f3icViGB4exsDAQEqPS6mzsbGB8fFxXLhwAdXV1fu+382bNzEwMMB2WKIsmZqaQl1d3YH+31L2eTweTE5O4tKlS6ioqACgvTaHw+Fd55kpigIAkGX5rbDMarXCZDJBkqSCqhLbTgiB5eVlzM/Po6ysDO3t7e+cmbe1tYXR0dEd31+XC/jCF4C/+AtAUQCzeQM1NVYYDLuXgIVCwMoKEI8DV68CP/MzQLqv601PT0NRFJw9eza9D0RERYuBGBEVpXA4nByEHwwGUVpaCofDAYfDkZEZGh9++CEGBgZyepv7YrW0tISFhYVDbel+69Yt9PX18d+VKEv43JpfhBCYmZmBz+fD5cuXD9wiqSjKjp0yE6FZOBxOtma6XC4cP34ctbW1O6rNcnVe1kF5vV7Mzs5CkiS0t7ejqqpq19tFIhEMDw+jtbUVoVA9fuIntNZIpxMoKwOi0QjC4ch7xzmEQsDLl9r9/vW/BtI1i39hYQHr6+u4dOkSLzIRUdpwqD4RFYVYLJYchL+xsQGTyQSHw4Fz585lZfhyVVUVVldXORw2hwghMDk5iVgshsHBwUOdUEuSBF5nIsoOIQSEEAzD8kQkEsHY2Bhqa2tx7dq1Q4UeOp0OpaWlKC0t3fXzQghsbGxgbGwMFosFgUAAKysrCAaDiMViyduZzeYdFWaJ0Cwffpaqq6tRXV0Nv9+P2dlZPHr0CC0tLaivr9/xPTUajRgYGMCf/ulD/Oqv2vH6tRXNzVJyJlhJSQk2N7cACAB7/1uYzUBLC/DsmVYl9u//PXDpUmq/ppcvX2JlZQW9vb0Mw4gorRiIEVFBUlU1OQh/bW0Ner0eNTU1aGlpQVlZWdbfYDmdTrx69YqBWI6IRqMYGRlBfX09mpubD30cWZahqmrK22yJ6P02NjYOtSMhZZ7X68XDhw9x8eLFPSuaUkGSJJSXl6O8vBxmsxn19fVv3UYIgVAolKwwW11dxdLSEoLBIFRVBaAFb2+GZVarFUajMevvJxJsNhu6u7sRjUYxPz+P2dlZNDY2oqmpKfmapKoy/vzPL2JxMYpjxzag09nxzfBLgtFYgkgkCqPR+M7HkmWgqQmYnwd+/ueBL31JqzJLBbfbjcXFRfT19eXM95aIChdbJomoIAghsL6+DpfLBY/HkxyE73A4UFlZmXNXeVVVxc2bN/GJT3wi20speuvr67h3715KTsyGh4fR1dX13pMJIkq9jz76CGazGcePH8/2UmgPQgjMzc3B4/HgypUrGWtbPOossXg8vusss3A4nLyN0WjcNTR71+6O6aSqKp4/f47FxUVUV1ejra0Nf/RHJvzLfwnU1gI6XQihUBDl5RXJ90iKEsfm5lZyztj7RKNapdgP/ADw+c8ffc1ra2uYnJxEf39/1r5vRFRc+ExDRHkrMQjf7XYjFouhvLwcTqcTp06dyvk3UrIsQ6/XIxqNFswck3z04sWL5JVok8l05OOxZZIoe7xeL7q6urK9DNpDNBrF2NgYqqqqMl79Y7FYYDQasba2dqgdJ/V6PcrKyvacryWEQDQaTYZlm5ubcLlcCAaDiMfjALTXh0Rr5vbQzGw2p+WinSzLaGpqwsmTJ+F2u3Hnzjh+/dfPArDBZtMDMEOn02FtbQ0VFeXQ6fTQ6fRQVRVCqJCk96+ppAQoLwe+8hXg+78fqKs7/Ho3Nzfx4MEDhmFElFF8tiGivBEOh+F2u5NvMm02G5xOJ3p6evKyIsfhcGBlZYXVDFmgqiomJyehqmpKB3AnWiaJKPMikUhKgm1KvbW1Ndy/f//AO/em0pkzZ9K246QkSTAajTAajXsGbqqqIhQKJUMzj8eDQCCAUCiUvJCi1+vfqjBLbABw2ABRkiQ4nU48euTE6qqC0tItrK0psFptMBpLUF5eDp9vHWVlZSgpKYHZbEIoFILFsveuldtVVwMLC8Cf/ZlWKXYYwWAQY2NjuH79Oi8SElFGMRAjopwVj8exsrICl8u1YxD+2bNn37m9eL5wOp2YmZlhIJZhkUgEIyMjOHHiBE6ePJnSYzMQI8qOcDiclxdGCp0QAvPz83C5XOjv78/qv9FRq8SOSpblZHXYXmKx2I62zLW1NQQCAUSj0eRtTCbTrqHZ+2ZX3r4NCKGDw1EORVEQCASwtbUFi8WCysoK+HzryYq1tTXfvgMxWdYqxf7qrw4XiEUiEQwNDR1qZ2cioqNiIEZEOUNVVayursLlcmF1dRU6nQ61tbVobm6G3W4vuOGqNpsNfr8/28soKj6fDxMTE7h06dK+Z6QchCzLbJkkygKPx8NNSnJMLBbD2NgYysvL0d/fnxOv4emsEksFg8EAu90Ou92+6+eFEAiHw8kZZuvr63j16hUCgUDyYowsy2+FZVarFZOTJpjN2r+BTqdDWVkZhFCTwZvRaEI4HEY8HocsS1CUOHS6/Z0q2mzA8+fA1hawx4afu4rFYrh79y66u7ths9n2f0ciohRhIEZEWZPYDj0xCF9VVVRVVcHpdOLMmTM5Nwg/1SRJSm4DXwgVb7nu2bNnePHiRVqrFCRJYoUYURZ4vV40NTVlexn0scTFh/Pnz+dUUJntKrGjSswhM5vNe24CoyhKMjALBoNwuVxYWQljdrYNkiSwuqoFXjqd7uMPPex2O2KxOCKRCKLRKCRJQiAQ3HNm2pssFsDr1Qbsnzu3v69FURQMDQ3h7Nmz3B2WiLKGgRgRZVQgEEgOwo9EIigvL4fD4UBra2tRDlGtq6uDy+VCS0tLtpdSsFRVxYMHDyDLMvr7+9MatLJlkig7NjY29qyqocwRQmBhYQGvXr1K2WYlqZbrVWJHpdPpUFpaitJtpVpuN2C1AiYTYLcLqKqAoihQlDgUJY5oVIGiKJAkCZIkIRwOIRQK7TsQ0+kARQFCof2tUQiB4eFhtLa25lRgSkTFp/jOPokooyKRSHIQfqISyul04tKlSzn5RjnTHA4HxsfHGYilSTgcxsjICJqamnDixIm0Px4DMaLMUxQFsiznREteMYvH4xgfH0dpaSkGBgZy9t8j36vEDmPndSAJsixBlmUYDIY97qGFZgf1njFm2pGFwNjYGOrr63Hs2LEDPwYRUSoxECOilIrH4/B4PHC5XFhfX4fRaITD4UBnZyfnQ+zCaDQiGo1CCJGzJw/5anV1FQ8ePEB3d3fG2jEkSeIMMaIM8/l8RRNs5Kr19XXcu3cPZ8+ehcPhyPZy3qvQq8TeVF6utTUGg8D+Cim10Gy/wmHAaAT26OLcYXJyEna7PeWb2hARHQYDMaJsi/kB/wIQdgNqHNAZAcsJwHYSkPe6cpc7VFXF2tpachC+LMuoqalBU1NTQQ7CT4eKigqe0KXY06dPsby8jIGBgYxu4c4KMaLM83g8qK6uzvYyitbTp0/x8uXLnG2R3E2xVYkZDEB7O3DnTnqOHwxqoVtDw7tvNz09DVmW0d7enp6FEBEdEAMxomyIBwH3B8DLPwA2HgHxAKBEtM9JEqCzAIYyoKYfOP7tQGU3IOXGgHkhBDY3Nz8e0roCVVVRWVkJp9OJzs7Ogh+Enw6JOWLF8KY83VRVxcTEBAwGQ1Z2NWOFGFHmra6uorW1NdvLKDrxeBz37t2DxWLJ6RbJvRRbldilS8CHHwJCaG81U8nvBz7xiXe3TD59+hTBYBCXLl1K7YMTER0BAzGiTBICcH8dmPl3gP8pAEkLvow1gGzS3qEIFVCCWkj24svA8h8DVVeAzp8GSrMzZyoQCMDtdsPtdiMcDsNut8PpdKKlpaUoB+GnWnV1NWZmZrK9jLwXCoUwMjKClpYWNLzvMnWasEKMKLOEEIjH4++YhUTpsLm5ifHxcXR2dsLpdGZ7OYdSbFVin/408F/+C7C+DlRUpO64oRCg1wPf+q173+bly5dwu93o7e3Nu+CUiAobz2SJMkUJA09+GVj6itYaaW4AdLu0ckkyoLdpHyaH1lK58iGw8QTo+BzQ8DdTf2nvDZFIBCsrK3C5XPD7/bBYLHA6nejq6sqbdoh8otPpIEkSFEWBbj8TaektXq8XDx8+RE9PT1Z3mmMgRpRZic1aKHOePXuG58+f49q1azCbzdlezpEUU5VYczPQ1wf82Z9pc8RSVdD/+jXQ1qYdezdutxuLi4vo6+tjGEZEOYeBGFEmKGHgwc8Ar/4CMFYDJQe4NGewAfpWIPQKePQLWuVY899L7fIUZccgfIPBAIfDgdOnT3MQfobU1NRgZWUFdXV12V5K3pmfn4fL5cr4vLDdyLLMlkmiDPJ4PKipqcn2MoqCoii4d+8ejEYjBgYGCmJEQrFVif34jwPj41qIVV9/9OOtrWnD9H/iJ7Q5ZW9/fg3T09Po7+8viJ8XIio8DMSI0k0I4Mm/0cIwc51W+XVQkgxYjgMhFzD7a9px6r7l0EtKDMJ3u93wer2QJAk1NTVobGzEhQsXeAUvC+rq6rC4uMhA7AASJ2cWiyVnrjxLksQKMaIM8nq9OH36dLaXUfASLZIdHR04duxYtpeTUsVWJfYjPwL84i9qYdZRMsBAAFhdBb7/+4HdvnWbm5t48OAB+vv7OV6DiHIWn52I0m3lQ+Dl7wPGqsOFYduZnUBgEZj+AlBxETDV7utuiUH4brcbKysriMfjyUH4p0+f5lW7HGC327G+vp7tZeSNYDCIkZERtLW1oT4Vl7lThC2TRJnFlsn0e/HiBZ4+fYre3l5YLJZsLyfliq1K7O/8HeDlS+BLXwLicaCm5uCTODY2gJUV4DOf0arD3rx/MBjE2NgYrl+/nvXKbSKid2EgRpROSgSY+VVAjQLm46k5prkBCDwF5v8zcPZn97xZMBiE2+2Gy+VCOBxGWVkZnE4nrl69yuHDOUiSJJhMJoTDYc5pew+Px4PJyUlcvnwZZWVl2V7ODrIsIxaLZXsZREUhFotBr9fnRHVoIVIUBffv34der8fg4GBBXzwrpioxWQZ+6qeA0lLgi18EFhaA48eB/bz1iMWA5WXtGN/zPcDP/izw5hi5SCSCoaEhXLlyJe9nzBFR4WMgRpROKzeBrQXAnMIKFlkPGOzAqz8DWn8YMFUDAKLRaHInyK2tLZjNZjidTly8eJFvSPKEw+GAy+XCyZMns72UnCSEwPz8PFZWVjA4OJiTwa4kSZwhRpQhq6urqKqqyvYyCpLf78fo6Cja29tzqgo3XYqtSkynA370R4HLl4Ff+RXgyRNtwkd5OWC1anPBEjlzNKq1R/p8gKoCJ04A//SfatVhb2bR8Xgcd+/exaVLl1BaWprxr4uI6KAYiBGl0/IfARCAzpja4xqrIfxPsTH7e3iOa/D5fNDr9XA4HGhvb4fNZuMV8zzkdDoxOTnJQGwXiqJgfHwcNpsN169fz9mfb7ZMEmWO1+uF0+nM9jIKztLSEubn53H16tWiakctpiqxhMuXgf/+34GbN4E/+APg/n3A7dZCsMTLrE6nhWRdXcB3fRfw6U8DuxVnK4qCu3fv4uzZs6ioOMDmUUREWcRAjChdlAjgewDoU3OFTACIxaKIRCKIRqMwxUOIee6h4dL34vz58zkbEND+WSwWhEIhCCH477lNYl5Ye3t7zg9zZiBGlDlra2scqJ9CqqriwYMHAIAbN24UdIvkboqtSizBZAK+5Vu0j/V1rYVyaQmIRAC9HqirA1pb3z1rTAiBkZERtLS0cNdXIsorDMSI0sW/CMT9gOFwV8kEtNLzSCSCaCQCVQgYDAaYTEatAiwUhlX/CqioOPg0VMpZZWVl2NzchN1uz/ZScoLb7cbjx49x+fLlvGi/YMskUWYIIaCqKnQ6XbaXUhACgQBGR0fR2tqKhoaGbC8na4qxSmy78nKgu1v72C8hBMbHx1FXV1cU7bVEVFgYiBGlS2RFqxIz7n9AuqIqiEQiiEQiUBQFer0BRqMR5RXlkKU3rtTKJiDqA5QQoC+8XZ+KldPphMvlKvpATAiB2dlZrK2tYXBwMG+2bGeFGFFmbGxsFP3zZKosLy9jdnYWV65cgc12xN2w81yxVokdxeTkJMrKytDU1JTtpRARHVhx1UITZZJQAIgDVW+FwxEAWpVQdVU1yu12mE2mt8MwQDuuEB8/DhUKh8OBlZWVbC8jq+LxOEZGRiCEwLVr1/ImDAMYiBFlitfrZWvWEamqivv37+P169e4ceNG0YdhCZ2dnXjy5Em2l5EXZmZmIEkS2tvbs70UIqJDYSBGlC6yCZB0BwqsrBYLLGYLdPI+WkBURTu+nOKB/ZRVer0eqqoWbagSCARw8+ZNnDx5EqdPn867WWpsmSTKDI/Hg+rq6mwvI28Fg0HcvHkTVVVV6OnpYevpNlarFUajET6fL9tLyWmLi4vw+/04d+5ctpdCRHRoDMSI0sXaCOgsgBJMz/GVIGBrAnQl6Tk+ZU1VVRW8Xm+2l5FxLpcLIyMjuHr1at7uHMcKMaLMiEQiMJn2P5KAvunVq1cYHh5Gd3c3Tpw4ke3l5KTOzk48fvw428vIWS9fvoTL5UJ3d3feXbgiItqOgRhRupjrAGO1Nlg/HdQoUM6rcoUoMUesWAghMD09jWfPnmFwcBBWqzXbSzo0BmJE6RcOh2E0sjr6oFRVxcOHD7G8vIwbN27kxUYl2cIqsb2trKzg6dOnuHr1KsMwIsp7DMSI0kWSAOengHhAm/WVSkoIkA1AdW9qj0s5oaqqCmtra9leRkbE43EMDQ1BlmVcvXo1r+aF7YYtk0Tp5/V62S55QKFQCLdu3YLdbsfly5fZIrkPrBJ7m8/nw+PHj3H9+nXIMk8jiSj/8ZmMKJ2OfSugLwViG6k9btgN2FqA6uupPS7lBEmSoNfrEY1Gs72UtNra2sLNmzfR0tKC9vb2grjSzAoxovTzeDwcqH8ALpcLQ0ND6OrqwsmTJ7O9nLzBKrGdtra2cP/+fVy/fj3vL14RESUwECNKp7J2wPFJILKSut0g435AqEDT9wMy35AUKofDAbfbne1lpM2rV68wPj6O3t5eOByObC8nZRiIEaXfxsYG7HZ7tpeR84QQmJqawvPnzzE4OIiysrJsLynvsEpMEwwGMTo6imvXrrFdmYgKCgMxonSSJOD0TwCWBiD44uitk2ocCL3SQrbj35mSJVJuKtQ5YkIIPH78GEtLSxgcHITFYsn2klKKLZNE6aUoCmRZLoiK0nQKh8O4efMmrFZrQbSjZwurxLQNLIaGhnDlyhWYzeZsL4eIKKUYiBGlm7kOOPMzgM4MhJYOH4qpcSCwCJSe0o4nc/5HISstLYXfn6YNGbIkFovh7t27KCkpwZUrVwpyhg0rxIjSy+fzoaKiItvLyGlutxt37tzBhQsX0NzcnO3l5L1irhKLx+O4e/cuLl26xE0YiKgg8XIRUSY4/zfgbAh4/AtAYAGwnADkkv3fP7YFhF9rYdilfwdYjqdvrZQzrFYrAoFAXu+6mLC5uYmxsTGcP3++oGf/MBAjSi+v11vQzyFHkajA9fv9uHHjBqvCUmR7lVgxhbGqquLu3bs4c+ZMUX3dRFRcWCFGlCnHvw3o/nWgrAMIPNdaH9XYu+8TD2hVYdFVoO6zwJXfAspOZWa9lHVOpxOvX7/O9jKObHl5GRMTE7h+/XrBn8iyZZIovbxeL6qqqrK9jJwTDodx69YtmM1mtkimQbFViQkhMDw8jJaWFtTW1mZ7OUREacNXS6JMqr4KXPsS8PS/AS+/8vFcMRWQjYDOBEiyNnw/HgQQ1/7efgY4+X8A9d+mfZ6KhsPhwPj4OFpbW7O9lEMRQuDRo0cIh8MYGBgoyBbJN7FCjCh9hBCIxWIwGAzZXkpO8Xg8mJycRHd3N8rLy7O9nIJUTFViQgiMj4+jrq4O9fX12V4OEVFaMRAjyjRDKdD+Y0Dz3wfc3wA2HgG+h0BwGYDQgrGKi0D5WaDyMlB1hfPCipTRaEQsFoMQIu8GSEejUYyOjqKurg7nzp3L9nIyJt/+nYjySTAYhM1my/YycoYQAtPT09jY2MDg4CCDwjTr7OzE/fv30d/fn+2lpNXU1BTKysrQ1NSU7aUQEaUdAzGibDHYtDbK49+m/VkIrTpM0mm7UxIBqKiowNraWl61CG1sbGB8fBwXLlxAdXV1tpdDRAXC4/HwOeVjkUgEo6OjcDqd6O3tZRifAcVQJTYzMwMAaG9vz/JKiIgyg/1XRLlCkgBZzzCMdnA6nXC5XNlexr4tLS3h/v37uH79Ok9ciSilPB5Pwc8h3A+v14vbt2/jzJkzOHXqFMOwDCrkWWKLi4vY2toqqqpuIiJWiBER5bDq6urkFdtcJoTA5OQk4vE4BgcHIcu83kJEqVUou+4elhACs7OzWF1dxcDAAEpKDrBbNaVEoVaJLS8v4/Xr17h27RoDViIqKjxjISLKYTqdDpIkIR6PZ3spe4pGo7h9+zZKS0vR3d3NMIyIUi4Wi0Gv1xftyXo0GsWdO3cgSRKuX7/OMCyLCq1KbGVlBQsLC2y9JaKixLMWIqIcV1tbi5WVlWwvY1fr6+u4desWOjs70dzcnO3lEFGBWl1dzatZiqm0urqKW7du4fTp02hvb2dokWXbq8Tync/nw+PHj3H9+nVezCKiosRnPiKiHJerc8SeP3+Ohw8foq+vr2hPVHcjhMj2EogKjtfrLbq5hIkWyenpafT39/N5NocUQpXY1tZWcuanXs8pOkRUnBiIERHlOLvdjo2NjWwvI0lVVdy/fz85x8ZkMmV7SURU4NbW1lBZWZntZWRMNBrF3bt3oaoq+vr6YDQas70k2ibfq8RCoRBGR0fR29vLny0iKmoMxIiIcpwkSTCZTAiFQtleCiKRCG7fvo2KigpcunSJLRa7YDsTUWoJIaCqKnQ6XbaXkhE+nw+3bt1Ce3s7Tp8+zeeUHJWvVWKRSARDQ0O4cuUKLBZLtpdDRJRVrI8lIsoDibbJpqamrK3B5/NhYmICly5dKqjdtYgot21sbMBut2d7GWknhMD8/DxcLhf6+/tZuZPj8nHHyXg8jqGhIXR1daG0tDTbyyEiyjpe2iciygNOpxNutztrj7+4uIipqSn09/fnzRt/IioMxTA/LBaLYXh4GLFYjGFYHsmnKjFVVTE0NITOzk6+jhMRfYyBGBFRHjCbzQiFQhkf2K6qKiYmJrCxsYGBgQGepBFRxhV6ILa+vo6bN2+ipaUFnZ2dbJHMI/kyS0wIgZGRETQ1NaG2tjbbyyEiyhkMxIiI8oTdbsfm5mbGHi8cDuPWrVuorq7GxYsXeZJGRFkRDodhNpuzvYyUE0JgYWEBk5OT6OvrY1CRp3K9SkwIgXv37sHpdOL48ePZXg4RUU5hIEZElCecTidev36dkcdaXV3FnTt3cOHCBZw4cSIjj0lE9KZwOFyQlanxeBwjIyMIhULcrTfP5XqV2NTUFGw2W1ZnkBIR5SoGYkREeaK2thYejyftj/P06VM8efIEAwMDKC8vT/vjERHtpRDbJTc2NvDhhx+iqakJZ8+eZfVtAcjVKrHZ2VkIIdDR0ZHtpRAR5STuMklElCf0ej1UVYWqqpDl1F/PSMwLKykpQX9/P0/SiCjrvF4vGhsbs72MlFlcXMSLFy9w/fr1gmwDLVa5uOPks2fPsLm5iZ6enmwvhYgoZ7FCjIgoj1RXV8Pr9ab8uKFQCDdv3oTD4cD58+cZhh1Rpjc/ICpUGxsbBVGpqigKRkdHsbW1hcHBQYZhBSiXqsSWl5fx6tUr9PT08PWciOgdGIgREeURp9MJl8uV0mN6vV7cvXsXXV1daGhoSOmxi5EkSQzEiFJAVVVIkpT3J/Sbm5v48MMP0dDQwAsOBSxXZol5PB4sLCzg6tWr/FkjInoPtkwSEeWRyspKTE1NIRYDlpYAvx+QJKC8HKivBw7aSTk/Pw+Xy4WBgQGUlJSkZc3FRpbltLW1EhWTtbW1nGk/O6znz59jcXERvb29sFgs2V4OpVlnZycePHiAvr6+rDy+z+fDo0ePMDAwAJ1Ol5U1EBHlEwZiRER5YmsL+NrXJHzpS2345/9chd8vIx7XPldSAlRUABcuAJ/9LDAwoP3dXhRFwb1792CxWNDX18eryCmUCMSI6GjyeaC+oiiYmJiAwWDA4OAgA/IiYbVaYTAYsjJLbGtrCxMTE+jv74dez1M8IqL94LMlEVGOi8WA3/1d4ItfBF6/BqLRathsMZSXG2EwaLeJRoG1NeBP/gT4sz8DWlqAH/1R4DOf0SrItgsGgxgZGUFbWxvq6+sz/wUVOFmW2TJJlAJerxfNzc3ZXsaBbW1tYWxsDB0dHTh27Fi2l0MZdubMGdy/fx/9/f0Ze8xQKITR0VFcu3YNRqMxY49LRJTvGIgREeWwpSXg538euHMHMBqBEycASZLh9/tRWvrNN70mE1BWpv0+HAaePgV++qeBmze1X+127XMrKyuYmprC5cuXUZa4A6WUJEmsECM6IiEE4vF43rVyLy0tJec3Wa3WbC+HssBqtaKkpCRjVWLRaBRDQ0O4fPky23KJiA6IgRgRUY56+hT43OeAmRmgoQH45vtcPeKJXsldmExAUxOwsQH83u9pVWVf+IKA1/sRPB4PBgcHYUiUllHKsWWS6OiCwWBendwrioIHDx5AlmW2SFLGqsTi8Tju3r2Lixcv8iIXEdEh8NWaiCgHra0BP/mTwNyc1v745nmhXq97ZygGaFVhjY3AnTsCP/iDKwgGY7h+/TrDsDRjIEZ0dF6vFzU1Ndlexr74/X7cvHkTDocDXV1dDMNoR5VYuqiqiqGhIZw+fRqVlZVpexwiokLGV2wiohwjBPBrvwY8eQKcPAnsNhvXaDQhEom891gGQxxWqw+PHlViauoMh+dngCRJnCFGdEQejycvBuq/fPkSY2NjuHLlCo4fP57t5VAOOXPmDB4/fpyWYwshMDIygqamJjgcjrQ8BhFRMWAgRkSUY4aGgD/6I6CmBtirmMtoNL43EItEIvD51uF0lqG01IDf+i3gxYs0LJh2YIUY0dH5/X7YbLZsL2NPqqpiYmICbrcbN27c4Lwweku6qsSEEJiYmIDD4WAIS0R0RAzEiIhyzJe/rA3Gf9csXm0nQxXAbpVIAn6/H8FgAFVVldDr9XA6AY9H24WS0osVYkRHE4/HodPpcraiNRAI4ObNm6ipqUF3dzdbJGlP6agSe/ToESwWS17uwEpElGv4Ck5ElEOeP9d2lNzPOBCDwYBoNLbj74RQP74aLVBRUQFJ0p7mZRmwWoE/+AMtbKP0YYUY0dGsrq7mbLvk8vIyRkZG0N3djYaGhmwvh3JcqqvE5ubmoCgKTp8+nZLjEREVO+4ySUSUQx4+BDY3tUH675OYI1ZSUgJAq6pYX19HaakNRqPprdtXVgIrK9qg/vPnU71ySmAgRnQ0Ho8n5+YiqaqKyclJxONx3LhxAzqdLttLojyxY8fJYBCYngbm54GXL4FoVBsUeuwY0NoKnD4N7LFb5LNnz7C+vo7Lly9n+CsgIipcDMSIiHLI/Lz26346cEpKSuD3+wEAkUgYW1t+VFSUQ6fb/andZAIiEe0xGIilDwMxoqNZW1vLqQqYYDCI0dFRNDU1obGxMdvLoTxjtVph8/kQ/Df/BpavfhVwubQgTJK0XXQA7fd6PVBVBXzrtwLf8R1AW1vyGK9evcLy8jKuX7+es63ERET5iIEYEVEOefYM2G/hQeJN8dbWJuJxBVVVVe98o5z41PLyERdJ78QZYkSHJ4SAqqo5U4H1+vVrTE9Po6enB2V7VO4Q7SkeB778ZZz7zd9EZGkJqK4GHA7AaPzmizKgBWOxGLC2Bvz2bwO/93vA3//7wD/4B/BsbWF+fh59fX0Mw4iIUoyBGBFRDolE9lcdlmAymbCxsQGjsQRra2vvvX0waMJHH61gaMgFWZYhSRJkWU5+vOvPB7ntfu6b+Cg0rBAjOrzNzc2cCJ6EEJiamkI4HGaLJB3O1hbwcz8HfPWr0BmNiDQ0QF9aipLdto+WJKCkBHA6tcBsZQX4tV9D5NYtzH7P9+Dat30bfwaJiNKAgRgRUQ4xmYCDZClWqxVWq3Wftxbw+YDW1hPo7q6HqqpQVTVZkbH9482/S/w58WssFnvn7Xb7826fO4pEyPZm2PaucO4ood5+jgUwECM6Co/Hg5qamqyuIRQKYXR0FCdOnMB59pfTYQQCwOc/D3z969p8MJsNpYqCzY0NVL5v1xxJAhwOxK1WxG/dwjVFgW5wUKsuIyKilGIgRkSUQ5qbAUVJz7GF0AKbxkYdSkry/0rzQcK494VziqIcKdjbHvD5fD7IsoyFhYUDfT2prsA76n0LsXqPcp/X68WFCxey9vhutxuPHj1CT08P7HZ71tZBee43fgP4xjeA48cBiwUAoNfpIMkyorHY7lVi2yiKAl84jMr2dugePAD+1b8CvvCFg5WQExHRezEQIyLKIa2t2sVhVU39+95QSBtb0tqa2uNmiyRJ0Ol0OddG8uzZM0iSdKDh20KI5MdRg7lEuHfUYx21gi+Tod5+W3Qp94XDYZjN5ow/rhACjx8/RiAQwI0bN6DX8y0yHdLdu8CXv6xt7fxxGJZQWlr63ioxVVXh8/lQXl4OnV4P1NUBf/VXwB/9EfCd35nmxRMRFRe+2hMR5ZCuLsBu1+bqpro7YnUVqK8HTp1K7XFpJ1mWoRywzG97YJNrAd9hvBnu7Tec2+3vFUVJtuge9L6pDvgOGsYdpJ33IO29hRrwRSIRlJSUZPxxw+EwRkdHcfz4cZw9ezbjj08FRAjgP/wH7QpUXd1bn35flZgqBNZ8PpTZ7TAkQtnSUu1NwW/9FvCZzwBZCIyJiAoVAzEiohxy7BjwyU8CX/mKtvt6qs55VRUIh4Hv/m5tbi+lD3eZ/GbAJxdIe0+qqvcSFXypONZRbP/3yVZL7pvhniRJ8Hq9GZ8ftrKygqmpKXR3d6O8vDyjj00FaGICePxYG4y/xwv4XlViQgj4fD6U2mxvh2V1dcDz58AHHwB//a+nafFERMWHgRgRUY75W38L+OpXtYquVFWJvXypbV71N/5Gao5He+NQ/cJTiAFfKtpq97vBxn4eBwBcLhfKy8vx6tWrA309+9lg483PAcDy8jJCoRDa29vh8/mwsbGRspCPitTXvqZVhx0/vudNdqsSEwB86+uwmM0wGo1v3ylxJevP/5yBGBFRCjEQIyLKMZcuAX/7bwO//dtap8Ru740PYmtLG9T/Yz+2awcHpRgDMcp1ifl7uebDDz/EwMDAgYPHgwZ74XAYk5OTqKqqQkdHx1sVfPsJ9t61O2+qdtBNZ/XeQXfnpX26f1/bLvo937PtVWICwMbGBowlJe+en2ezAY8eAfE4wBl3REQpwWdTIqIc9MM/rHVe3LsHnDx5+DbHQAB4/Rr49m8Hvuu7UrpE2gNbJokOLhEiH6YK7yAbbHg8HszOzuLatWuoqKg48GOl23422NhvALifDTb2c6x0b7CR7g01dmvRTQu/X2trtFrfe9PtVWLhcBg6nQ7W993PYgE2NoClJaCpKUWLJiIqbgzEiIhyUGmptsP65z4HPHigtTuWle3//kJoLZc+H/At3wL8i38B5GBBSEFihRjRwfl8vrQGVEIIzMzMwOfzYXBwEIZdBprngmLdYGM/wdxeG2wcpEIwFc/NewVuJV4vWtbXoVosEJubgCRBAr4ZwH38Z3x80USSJHg8HthsNpSVlr7/gUtKgFhMC8WIiCglGIgREeWoujrgN38T+JVfAf7iL7RNphyOd28wJYRWFeZ2a7f7gR/QWiW5KVXmMBAjOjiPx5O2gfqRSARjY2Oora3FtWvX2AKYQYU4f2/PUO/lS5hMJqglJYgZDFAUJfmhKgqUxOuCEJA+DtFsNhvKDnK1i4iIUoqBGBFRDqup0QKxwUFtptjCgjY+xGzWujISY0SiUS0IC4e1z3V1AT/+40Bvb3bXX4zYMkl0cKurq2hubk75cb1eLx4+fIiLFy+iqqoq5cen4iKEQDAYRDAYRCAQSP4+FApB9vtxLhKBFItB6PXQ6XQwGo3Jdl5ZlnGkKDYWAwwGrYSciIhSgoEYEVGOkyTg274N+OxngTt3gG98Q2ujdLu1AAzQ3iOfPg1cvAh8+tPaYH4WQWQHK8SIDkYIgWg0ipLDDkvc45hzc3PweDwYGBhI6bGpMCU2XNgedCWCr+0z7iwWS/KjtrYWVqsVJpNJq4L74heBjz4C7PbULzAQ0MKwEydSf2wioiLFQIyIKE8YDMAnPqF9CKHNB/P7teCrvJwXjXMFAzGigwkGg+8fKH4A0WgUY2NjqKqqQl9fH1skKRm6vlnZFQwGEYvFAGjVvSaTCVarFRaLBRUVFaivr4fFYtn/PLeLF4GpqfR8EX4/0N2tvRkgIqKUYCBGRJSHJAmorNQ+KLewZZLoYLxeL6qrq1NyrLW1Ndy/fx8XLlxI2TEp98Xj8R1BV+L3kUgkeRuj0Zis7LLZbHA4HLBYLKndYOGTnwR+93eBYFDbFTJVPg7t8JnPpO6YRETEQIyIiCiVWCFGdDBerxdtbW1HOoYQAvPz83C5XOjv74fRaEzR6ijbFEVBKBTadW5X4uKDXq+HxWLZUd1lsVhgNBozWyF45QrQ3g48eQKkciae2w0cOwZ86lOpOyYRETEQIyIiSiUGYkQHs7W1BZvNduj7x2IxjI+Pw263o7+/ny2SeUQIkQy73gy8dpvbZbVak5VdZrM59/6t9Xrgh38Y+KmfAtbXtXkGRxUIaDvn/MN/yNkIREQpxkCMiIgohWRZZssk0T7F43HodLpDBxs+nw8TExM4f/48ampqUrw6Ooq95nYFAgHE43EAb8/tqqysxPHjxw82tyvXfOpT2k44/+t/ASaT9nFY8TiwvKwND/3e703ZEomISMNAjIiIKIUkSWKFGNE+ra6uoqqq6sD3E0JgYWEBr169Ql9fH0xHCR3oUGKx2Fu7MWZlbleukSTgJ38SePECGB4GGhoAs/ngx4lGgefPtS2k/9k/06rPiIgopfjMSkRElEJsmSR6m6qo2FzahO+pD5EtLTAxlhnhVbxoPNd4oGPF43GMj4+jtLQUAwMDudc2VwAURdkRdiUCr3A4vGNuV6KyK1HdZbFYUFJSwn+T8nLgC18APv954O5doKwMqK3VwrL3EQJYWwNWV4Hz54F/+2+B+vq0L5mIqBhJgn0dREREKaMoCoaGhtDf35/tpRBlne+pD3N/Moe5P51DaDWEWDCWDFQkSUJEjaCmsQYd39GBU3/jFMpPlr/zeOvr67h37x7Onj0Lh8ORga+g8Bxmblfi9zk5tyuXhcPAf/2vwH/7b9pMMbsdqKravdpLUQCfT/uwWoHv+R7gR39Uuw8REaUFAzEiIqIUUlUVd+7cwcDAQLaXQpQ14Y0wxv7jGGa+MoPIRgQGqwHGUiMMVgNknQwAUOIKPK88sMgWxIIxGMuNOPO9Z9Dzwz0osZW8dcynT5/i5cuXuHLlClsk95CY27U95Hrf3K7tH3k7tyvXTU8Dv//7wF/8hVb5lTj9kmVge0Wx3Q588pPA3/ybQE9PdtZKRFREGIgRERGlkBACt27dwuDgYLaXQpQVK49W8PWf+zq8016YK80wV+1eVZSYQWW32yFUgeBqEOH1MGrP1uKv/cJfQ3VHNQCtRfLevXuwWCw4e/ZsUVcoJb5nbwZeu83tejPwKui5XflidRWYnAQ++gh49gyIRICSEm3OWGsrcO4cUFeX7VUSERUNBmJEREQpdvPmTQZiVJRcD134y5/4S2wtb6G8sRy6kr0rjgKBAGRZhnnbwHElqmD9+TrsJ+z47L//LIzHjRgfH0dnZyecTmcmvoSsOczcrsTvObeLiIjo4DhUn4iIiIiOzO/y468+/1fwv/KjsqUSkvzugCYajaKsrGzH3+lKdKhoroDvqQ9/+GN/iMZ/0oj+b+nfEZrlo+1zu96s7tprbldiR0bO7SIiIkoPBmJEREREdCRCCAx9YQi+RR8qW98fhgGAoiq7zqySZAlSpYT1p+toGmuC6dtze16YEAKRSGTXIfXb53aZzeZk4FVVVYWGhgaYzWbO7SIiIsoSBmJERESpIgQQW0eJsgqEV4CSSkDmSy0Vvuc3n2P+/5tHaV1pcmj+uyiqAll++3bxeBzr6+uw2WywNFjw0Z9+hPb/vR3He4+nY9n7ctC5XWVlZXA6nbBardDvtpsgERER5QS+ShMRER1FPAC4vg54h4D1SSDixZnNdeCDMkBvBeydQOUloO4zgCV7J/VE6TTzlRkoUQXGMuO+bh+NRlFSsnMnyVAohEAwgIqKCq1qygQEPAHM/OFM2gKx/cztMhgMO4bTV1VVcW4XERFRAWAgRkREdBhKBHj2P4FnvwOElgEIQGcGdBYokhGQTVpY5v4AcH0NmP9PgOOvAW0/Bljqs716opTZeLGBpbtLsFRZ9n2faDSanAsmhMDGxgYkWUJVZdWOkMlcYcazbzyD3+WHzWk70LpUVUU4HH6rsuvNuV3bh9Q7HA5YrVaYTCaGXURERAWOgRgREdFBbc4Cj/41sDaqBV+WBkD+ZrWLIqtAiR2AXfsLoQJRH7D0+8DqCND+T4D6bwd4wk0FwPPEg+hWFBXNFfu+TzwWh6HUkGyRtNqsMJveHpxvKjdh/fk6PNOeHYHYUeZ2WSyWXds1iYiIqLgwECMiIjoI3wNg4ieB4LLWAqnbx+53kgwYq4CSciD4Epj850DIBbT+I4ZilPfW5tcghNjXIH0AyVbERPVWeUU59Lqdb0lVVYWiKFAUBdFIFJMfTMJtdr81tytR3cW5XURERHRQfMdARES0X1vzwMRPAaFXgK1FC7oOQtIB1kZt4P5H/wEw2IGTfzs9ayXKEL/bf6D2wmg0img0CgEBs9mMYDAIRVGgKmryNpIsQafTQafTaZVeqhldXV2c20VEREQpw0CMiIhoP5Qo8PgXgeASYGs9eBi2nalWqzCb+w1t4H5ZW+rWSZRhalx9/4220el00Ol1MBlNkGUZBoMBOp0Osk6GhLfDrog+AmOJEUbj/gb2ExEREe0HBygQERHtx4svaztJmo8fLQxLMB8DIqvAk1/WZowR5SmDxZBsg9wPvV6P2ppa2Gw2mM1mlJSUaJVgu4RhyfuYeA2XiIiIUouBGBER0fsoUeD57wCSAdDvfye9d5IkwFwHrN3TPojyVEVTBSDhQKHYfiWOWdG0/4H9RERERPvBQIyIiOh9PLcA/6LW6phKOiughIGXf5za4xJlUGVrJWS9DCWqpPzY8XAceqMela2VKT82ERERFTcGYkRERO+zOg4IBdCZUntcSQL0pVrgpsZTe2yiDHFccKC0rhRBTzDlxw56grCfsKOmsyblxyYiIqLixkCMiIjofdanALkkPcfWW4HYJhB8kZ7jE6WZwWxAx3d1IBaM7dgp8qhURUU8EkfHd3VAV6JL2XGJiIiIAAZiRERE76YqQOAZoDMf6G77nqakswBKCAgsHXRlRDmj4zs7UHq8FFvLWyk75sbSBuyNdrR/e3vKjklERESUwECMiIjoXUQMgApI+69QMZkO0Fopydouk2r04GsjyhGldaW48mNXoKoqwhvhIx8v5AtBkiT0fq4XluoUbWRBREREtA0DMSIioneR9NBeLve/g57VYoW071ur2iwxWX/wtRHlkI7v6EDHd3YgsBJAZDNy6OOE18MIrYVw5vvOoPWzrSlcIREREdE38d03ERHRu8h6wFwHbH2UnuMrYW1Yv6kuPccnyhBJljD4c4MQisDMH84g6o/CVmeDJO0vHhaqwNarLSgxBWe+9wz6Pt+37/sSERERHRQDMSIiovcpP68N1k+HeADQ2wBbU3qOT5RBeqMen/iXn0BVexXu/ad7WP1oFZZKC8yVZkjy7uGWqqgIrYUQWgvB5rCh5x/34Mz3noGsZyMDERERpY8khNh/DwgREVExevnHwIOfBiwNqd9t0j8P1H0W6P53qT0uUZatzq3i/hfv49k3nmlzxYS2I6Vs0IIuNaYiFo4BAMwVZjR9qgld/6ALFc0V2Vw2ERERFQkGYkRERO8T8wM3vwOIrgPmY6k7rhICwi6g5zcAxydTd1yiHLK5vInnHz6Hd9aLlckVhNe1ofumChMc5x2o7qhG441GlNaVZnmlREREVEwYiBEREe3H3H8EZn8dsDampkpMCCDwFLCfAa7/T0CX4sozIiIiIiLaE4czEBER7UfT/wWUnwOCL7Qw66iiq4DOApz+vxmGERERERFlGAMxIiKi/TDYgM6fBkrKgdARQ7HYBhBbB5r+LlB9JVUrJCIiIiKifWIgRkREtF9VPcDZf6FVdgUWATV+sPsLAYRXgIgXOPG9QNuPpWWZRERERET0bpwhRkREdFArt4EnvwhszQMlldqH9J5rTPEAEH4N6Eu19stTPwLI+sysl4iIiIiIdmAgRkREdBhhLzD3m8CrP9faH2UToLcCegsg6bRqMDWsBWHxAKAzAvazwOmfAiq7sr16IiIiIqKixkCMiIjoKAIvgFd/Cri/AQSWACUECAWApIVgxmqg8jJQ/61A1eX3V5IREREREVHaMRAjIiJKleiGtgulEgYkPWByAOY6QJKyvTIiIiIiItqGgRgRERERERERERUV9m0QEREREREREVFRYSBGRERERERERERFhYEYEREREREREREVFQZiRERERERERERUVBiIERERERERERFRUWEgRkRERERERERERYWBGBERERERERERFRUGYkREREREREREVFQYiBERERERERERUVFhIEZEREREREREREWFgRgRERERERERERUVBmJERERERERERFRUGIgREREREREREVFRYSBGRERERERERERFhYEYEREREREREREVFQZiRERERERERERUVBiIERERERERERFRUWEgRkRERERERERERYWBGBERERERERERFRUGYkREREREREREVFQYiBERERERERERUVFhIEZEREREREREREWFgRgRERERERERERUVBmJERERERERERFRUGIgREREREREREVFRYSBGRERERERERERFhYEYEREREREREREVFQZiRERERERERERUVBiIERERERERERFRUWEgRkRERERERERERYWBGBERERERERERFRUGYkREREREREREVFQYiBERERERERERUVFhIEZEREREREREREWFgRgRERERERERERUVBmJERERERERERFRUGIgREREREREREVFRYSBGRERERERERERFhYEYEREREREREREVFQZiRERERERERERUVBiIERERERERERFRUWEgRkRERERERERERYWBGBERERERERERFRUGYkREREREREREVFQYiBERERERERERUVFhIEZEREREREREREWFgRgRERERERERERUVBmJERERERERERFRUGIgREREREREREVFRYSBGRERERERERERFhYEYEREREREREREVFQZiRERERERERERUVBiIERERERERERFRUWEgRkRERERERERERYWBGBERERERERERFRUGYkREREREREREVFQYiBERERERERERUVFhIEZEREREREREREWFgRgRERERERERERUVBmJERERERERERFRUGIgREREREREREVFRYSBGRERERERERERFhYEYEREREREREREVFQZiRERERERERERUVBiIERERERERERFRUfn/AY047xHlc6mvAAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "✅ Analysis complete! AI Agent successfully analyzed biomedical data.\n"
          ]
        }
      ]
    }
  ]
}